Model Jobs in Jordan

<p><h4>Description</h4> <p>The data cleansing program director leads the government-wide data cleansing program — driving large-scale data quality uplift across multiple government entities. You will own the cleansing strategy, orchestrate a multi-vendor delivery model, oversee enterprise data modelling, and ensure that cleansed datasets are published into the government-wide catalogue and sharing platforms — sustainably, with strong governance, ongoing monitoring, and continuous improvement.</p> <ul> <li><strong>Define and lead the government-wide data cleansing strategy</strong> aligned with data quality standards, policies, and government priorities.</li> <li><strong>Establish a structured framework</strong> to prioritize critical datasets and drive phased execution across entities.</li> <li><strong>Standardize, automate, and scale the end-to-end data cleansing lifecycle</strong> — leveraging advanced tools and AI-driven solutions; govern reusable accelerators.</li> <li><strong>Establish and oversee a multi-vendor delivery model</strong> — coordinated execution, performance management, and scalability across entities.</li> <li><strong>Oversee large-scale data quality improvement initiatives</strong> aligned to defined quality dimensions, rules, and thresholds.</li> <li><strong>Oversee development of entities' enterprise data models</strong> — aligned with government-wide data standards, central information models, and interoperability requirements.</li> <li><strong>Ensure creation, standardization, and publication of cleansed datasets</strong> into the government-wide data catalogue and sharing platforms — enabling accessibility and reuse.</li> <li><strong>Oversee development of data quality dashboards</strong> to track KPIs and cleansing progress.</li> <li><strong>Establish and lead program governance bodies</strong> — driving timely, quality delivery, issue resolution, and stakeholder alignment.</li> <li><strong>Establish and govern sustainable data quality and cleansing frameworks</strong> for long-term maintenance and continuous improvement.</li> <li><strong>Provide regular reporting to senior leadership</strong> on program progress, risks, and impact on data maturity and value realization.</li> </ul> <h4>Requirements</h4> <ul> <li>Bachelor's or master's in data management, IT, engineering, or related field.</li> <li>20+ years in data management, data quality, or large-scale transformation programs (preferably with exposure in Europe or North America).</li> <li>Proven track record leading enterprise-wide or national data cleansing / data quality initiatives, including multi-vendor delivery models.</li> <li>Strong expertise in data quality frameworks, profiling, and remediation techniques.</li> <li>Familiarity with modern data platforms — Informatica, Azure, Databricks, Snowflake, or similar.</li> <li>Demonstrated experience managing large vendor ecosystems and complex multi-stakeholder programs.</li> <li>Strong leadership and stakeholder management skills at senior government or enterprise level.</li> <li>Excellent English communication; Arabic a plus.</li> </ul></p><p></p>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br>What this opportunity involves: We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT: Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for: 8+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard: Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidEffort estimate Tasks for this project are estimated to take 30 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation: Up to $150/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~30 hours each; you set your own schedule.<br></span> </div>

<p><h4>Please submit your CV in English and indicate your level of English proficiency.<\/h4>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves:<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n <li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n <li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n <li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n <li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is NOT:<\/h4>\n<ul>\n <li>Not data labeling<\/li>\n <li>Not prompt engineering<\/li>\n <li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for:<\/h4>\n<ul>\n <li>8+ years in software development<\/li>\n <li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n <li>Experience writing tests (functional, integration)<\/li>\n <li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard:<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply ? Pass qualification(s) ? Join a project ? Complete tasks ? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 30 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p><\/p><p><\/p>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for 5+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Effort estimate Tasks for this project are estimated to take 20 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation Up to $50/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~20 hours each; you set your own schedule.<br></span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for 5+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Effort estimate Tasks for this project are estimated to take 20 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation Up to $50/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~20 hours each; you set your own schedule.<br></span> </div>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>We are looking for a talented Sr. Specialist. Data Scientist to join us. At Hikma you ll be supported by a culture of progress and belonging where people are encouraged to develop, wellbeing is prioritised and our inclusive approach values contributions from all. We re seeking candidates who embody our values: Innovative, driven to keep learning; Caring, genuinely compassionate in their work; and Collaborative, eager to solve problems together.</p><p>If you want to be part of a team that cares about impact, this is the place for you.</p><h3>Key Responsibilities:</h3><ul><li>Develop and deploy machine learning models to enhance financial forecasting, anomaly detection, and predictive analytics.</li><li>Work with large datasets from SAP ECC, ERP systems, and external sources, integrating them into Azure Databricks for analytics.</li><li>Collaborate with finance, accounting, and business teams to understand challenges and develop data-driven solutions.</li><li>Design and optimize data pipelines to enable ML model training, validation, and deployment.</li><li>Utilize LLM, NLP, time-series analysis, and deep learning where applicable to extract insights from structured and unstructured data.</li><li>Develop interactive dashboards and visualizations to communicate insights to finance and business stakeholders.</li><li>Ensure data quality, governance, and compliance while handling sensitive financial data.</li><li>Utilize automation tools such as Selenium to streamline data collection and processing workflows</li></ul></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><p>We are looking for candidates whose experience and skills align closely with the qualifications outlined below:</p><p>Minimum: Bachelor s in Data Science, Computer Science, Statistics, Mathematics, or a related field.</p><p>Preferred: Master s degree in Data Science or a related field.</p><p>At least 4-7 years experience in data science and machine learning.</p><p>Experience in a Finance department is plus.</p><h3>Skills:</h3><p>Strong proficiency in Python (including Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).</p><p>Experience with Databricks and Azure Databricks for large scale data processing and ML model deployment.</p><p>Hands-on experience with SQL and working with relational databases.</p><p>Knowledge of big data processing frameworks such as PySpark.</p><p>Expertise in ETL/ELT processes and working with structured and unstructured datasets.</p><p>Experience with data visualization tools such as Power BI, Tableau.</p><p>Strong problem-solving, analytical, and critical-thinking skills.</p><p>Experience with LLMs and NLP-based solutions for data enrichment and smart automation.</p><p>Proficiency in English, both written and verbal, for effective communication with finance and business stakeholders.</p><p></p></section>

<p>To lead the Wealth Management function by overseeing investment portfolios, developing investment and bancassurance products, enhancing the service model for affluent clients, driving client acquisition and asset growth (AUM), delivering financial advisory services, ensuring regulatory compliance, developing the team, and monitoring KPIs to improve profitability, operational excellence, and the Bank's competitive position.</p><p>Responsibilities</p><ul><li>Oversee departmental operations and staff, ensuring efficient performance, goal achievement, and business continuity.</li><li>Develop Wealth Management policies, strategies, and plans to achieve business growth, profitability, and market share.</li><li>Design and enhance the Wealth Management service model, operating framework, digital capabilities, and team development.</li><li>Develop and implement marketing strategies and campaigns for Wealth Management and bancassurance products.</li><li>Manage Bancassurance operations and develop insurance solutions that enhance client value and Bank revenue.</li><li>Oversee investment portfolio management and asset allocation, ensuring solutions align with clients' objectives and risk profiles.</li><li>Lead client acquisition initiatives by targeting affluent clients, converting existing customers, and supporting portfolio growth.</li><li>Oversee comprehensive financial advisory services and integrated banking solutions for Wealth Management clients.</li><li>Develop new revenue streams, monitor AUM, fee income, KPIs, and market trends to maximize profitability.</li><li>Lead team training and promote integrated banking, investment, and insurance solutions to drive cross-selling.</li><li>Oversee Wealth Management client relationships, ensuring efficient service delivery and strong client retention.</li><li>Drive growth in Assets Under Management (AUM), expand the client base, monitor KPIs, and report performance.</li><li>Ensure compliance with Central Bank regulations and obtain required approvals for investment and insurance products.</li><li>Support the development of Service Level Agreements (SLAs) to ensure efficient service delivery.</li><li>Ensure daily operations comply with approved policies, procedures, and delegated authorities.</li><li>Review operational and audit reports, strengthen internal controls, and ensure timely resolution of audit findings.</li><li>Participate in Business Continuity Plan (BCP) and emergency response activities to ensure operational resilience.</li></ul><p><strong>Desired Candidate Profile</strong></p><p>Bachelor's degree in Business Administration, Banking, Finance, or a related discipline. Minimum of 10 years of banking experience, including at least 5 years in Wealth Management , encompassing Bancassurance. Experience in investment products, market analysis, portfolio management, risk and return assessment, and asset allocation. Experience in Bancassurance is preferred. Strong knowledge of the banking market and its various sectors. Good understanding of internal and external banking policies and procedures. Proficiency in computer applications and banking systems. Knowledge of the regulations and laws issued by the Central Bank of Jordan and other regulatory authorities. Comprehensive knowledge of the Banking industry's products and services.</p>

<p>About the Role As a Data Scientist at Aspire, you will be responsible for building and maintaining production machine learning solutions in a cloud environment. This role focuses on developing scalable ML models, collaborating with cross-functional teams, and supporting data-driven decision making across the organization. You will work within US time zones (PST to EST) and operate in a remote-first, distributed team environment.</p><p><b>What You'll Do</b></p><ul><li>Develop, deploy, and maintain production machine learning models.</li><li>Perform exploratory data analysis and feature engineering to extract actionable insights.</li><li>Build and improve data pipelines that support ML workflows and automation.</li><li>Collaborate with engineering and business stakeholders to deliver scalable ML solutions.</li><li>Contribute to AI/LLM-based capabilities where applicable to enhance product offerings.</li><li>Monitor and optimize model performance in production environments (not only notebook-based development).</li><li>Document all procedures, configurations, and changes in a clear, auditable manner.</li></ul><p><strong>Desired Candidate Profile</strong></p><p>What You'll Need</p><ul><li>2+ years of experience building and maintaining production machine learning models.</li><li>Strong Python programming skills.</li><li>Experience with AWS SageMaker or another enterprise ML platform (e.g., Vertex AI or Azure ML) supporting production ML pipelines.</li><li>Experience deploying and monitoring ML models in production (not only notebook-based development).</li><li>Advanced SQL.</li><li>Git/version control.</li><li>Ability to work independently in production environments.</li><li>Experience with MLOps tools (MLflow, Airflow, dbt, or similar).</li><li>Snowflake.</li><li>Marketing, growth, experimentation, or causal inference experience.</li><li>Experience with LLMs or AI agents.</li><li>Agile development practices.</li><li>Experience building scalable ML solutions in enterprise environments.</li><li>Familiarity with cloud-based ML platforms and production deployment best practices.</li><li>Strong communication skills and ability to work with cross-functional teams.</li><li>Familiarity with US time zones (PST to EST) and remote collaboration workflows.</li></ul>

<p><h4>Description<\/h4>\n<p><strong>About Open<\/strong><br>\nOpen.cx is the unified AI customer support platform automating 70%+ of interactions for enterprises like MoneyGram and Mollie. Backed by Y Combinator, X by Unifonic, and Shorooq Partners, we recently raised $7M+ to scale our operations across MENA and globally. We are moving from a startup phase to a high-growth scale-up, and we need a financial architect to build the foundation for that growth \u2014 and stay with us all the way to the next decade of scale.<\/p>\n\n<h4>Your mandate<\/h4>\n<p>Own the financial engine of a company on a 3\u20135x ARR year, scaling from a handful of enterprise logos to category-contender status.<br>\nBuild the planning, reporting, and operating cadence that lets every team \u2014 GTM, product, ops \u2014 make sharper decisions, faster.<br>\nOwn the next two priced rounds. Build the model the leads will actually pressure-test. Quarterback diligence. Sit at the table for term sheets.<br>\nBe the partner who tells the founders the truth about the numbers \u2014 and the strategist who shapes pricing, packaging, market expansion, and headcount because of them.<br>\nIf you're successful, you'll materially shape Open's growth trajectory and be the finance leader on the cap table at every subsequent round.<\/p>\n\n<h4>What you'll do \u2014 year one<\/h4>\n<p>Today, finance at Open lives in the founders' heads, a few spreadsheets, and our outsourced accounting partner. Your first major mission is to turn that into a structured, compounding system \u2014 a single source of truth on revenue, cash, burn, runway, unit economics, and pipeline that the leadership team trusts and acts on every week \u2014 and that the Series A leads will buy.<\/p>\n\n<p><strong>First 100 days<\/strong><\/p>\n<ul>\n<li>Listen tour: founders, every GTM leader, every customer-facing team. Map how money actually moves today.<\/li>\n<li>Stand up the weekly metrics email and the monthly board pack v1.<\/li>\n<li>Clean up the chart of accounts, compress the close to 5 business days.<\/li>\n<li>Build the v1 forecast model that survives contact with reality. Run it weekly.<\/li>\n<li>Pick the finance stack (NetSuite or QBO + Stripe\/Maxio + Brex\/Ramp\/Pleo + Pigment\/Cube\/Mosaic).<\/li>\n<li>Diagnose the unit economics: AI cost per resolved ticket, gross margin by segment, CAC payback by channel.<\/li>\n<\/ul>\n\n<p><strong>Months 4\u20139<\/strong><\/p>\n<ul>\n<li>Quarterback the Series A. Model, narrative, data room, diligence, term-sheet review.<\/li>\n<li>Stand up multi-entity consolidation cleanly (EU\/US subsidiaries).<\/li>\n<li>Build the pricing and deal-desk function \u2014 enterprise contract reviews, custom terms, ASC 606 compliance.<\/li>\n<li>Establish board cadence and investor reporting that the next round's leads will trust.<\/li>\n<\/ul>\n\n<p><strong>Months 10\u201318<\/strong><\/p>\n<ul>\n<li>Hit the targeted ARR WIG. Be the finance leader at the table for every enterprise close.<\/li>\n<li>Stand up audit-readiness in advance of Series B.<\/li>\n<li>Partner with CEO on geographic expansion math, M&A signals (acquihires, tuck-ins), and pricing 2.0.<\/li>\n<\/ul>\n\n<p><strong>Year 2 and beyond<\/strong><br>\nSeries B prep and close. Team scales to 7\u201312. Audit, tax, treasury, FX program.<\/p>\n\n<h4>Requirements<\/h4>\n<h4>What makes us excited about you<\/h4>\n<p>You're a player-coach, and you're proud of it.<br>\nYou're not above the spreadsheet. You can build the model on a Saturday and present it to a Sequoia partner on Monday. You're hiring your first report, but you're also the one who fixes the broken VLOOKUP at 9pm because the board pack ships tomorrow. You'll grow out of the spreadsheet work \u2014 but not for 12 months, and you're clear-eyed about that.<\/p>\n\n<p>You're a first-principles thinker who loves to build.<br>\nYou see chaos \u2014 disconnected systems, copy-pasted spreadsheets, inconsistent definitions of ARR \u2014 and your brain instantly starts drawing the clean version. You have strong opinions on what \"good\" looks like and you know how to get there one quarter at a time.<\/p>\n\n<p>You're commercial, not just operational.<br>\nYou're as comfortable in a pricing conversation as a close. You can read a usage-based contract and tell the AE what to push back on. You've sat across from a CRO and made them change their mind on a pricing tier with one chart.<\/p>\n\n<p>You're AI-native \u2014 and you can prove it.<br>\nPulling from Salesforce, Stripe, accounting platforms, billing, and spreadsheets doesn't intimidate you. SQL is a plus. But what we really care about: you've already wired AI into your finance workflow \u2014 Claude, MCP servers, Cube, Pigment, Mosaic, custom GPTs, agentic automations. You can show us a workflow you built last quarter that compressed a finance task by 5\u201310x. You read MCP server release notes for fun. When a new tool drops, you've tried it before your CEO sends you the link.<br>\nWe are an AI company. Our finance leader uses AI like our engineers do \u2014 as the default, not the exception. If \"I'm excited to learn about AI\" is your answer to this, you're not the right person for this seat.<\/p>\n\n<p>You care about outcomes, not output.<br>\nYou want to move charts and real numbers \u2014 runway extended, gross margin lifted, fundraise closed at a better valuation. You're comfortable designing the metric and proving impact.<\/p>\n\n<p>You can sit at any table.<br>\nYou can sit with our CEO, an enterprise CFO at a $5B payments company, a Sequoia partner, a Jordan-based engineer, and an SDR \u2014 and translate seamlessly between them.<\/p>\n\n<p>You're creative and unafraid to be wrong.<br>\nYou generate ideas constantly, test them quickly, and iterate without ego. You'll change your mind in public when the numbers say so.<\/p>\n\n<h4>Bonus<\/h4>\n<ul>\n<li>Experience at a YC-backed SaaS company, ideally infra\/AI\/CX.<\/li>\n<li>Multi-entity, multi-currency operating experience (especially MENA + EU + US).<\/li>\n<li>Lived through one IPO, acquisition, or unicorn round as part of the finance team.<\/li>\n<li>Network into top-tier Series A\/B investors who would back our next round.<\/li>\n<li>Understand usage-based or hybrid pricing models in B2B SaaS.<\/li>\n<li>You've used an MCP server, custom GPT, or Claude-driven workflow inside a finance org. We will ask to see it.<\/li>\n<li>You contribute publicly on AI-in-finance \u2014 posts, talks, GitHub, Substack \u2014 even casually.<\/li>\n<\/ul><\/p><p><\/p>

<p><h4>Description<\/h4>\n<p><strong>About Open<\/strong><br>\nOpen.cx is the unified AI customer support platform automating 70%+ of interactions for enterprises like MoneyGram and Mollie. Backed by Y Combinator, X by Unifonic, and Shorooq Partners, we recently raised $7M+ to scale our operations across MENA and globally. We are moving from a startup phase to a high-growth scale-up, and we need a financial architect to build the foundation for that growth \u2014 and stay with us all the way to the next decade of scale.<\/p>\n\n<h4>Your mandate<\/h4>\n<p>Own the financial engine of a company on a 3\u20135x ARR year, scaling from a handful of enterprise logos to category-contender status.<br>\nBuild the planning, reporting, and operating cadence that lets every team \u2014 GTM, product, ops \u2014 make sharper decisions, faster.<br>\nOwn the next two priced rounds. Build the model the leads will actually pressure-test. Quarterback diligence. Sit at the table for term sheets.<br>\nBe the partner who tells the founders the truth about the numbers \u2014 and the strategist who shapes pricing, packaging, market expansion, and headcount because of them.<br>\nIf you're successful, you'll materially shape Open's growth trajectory and be the finance leader on the cap table at every subsequent round.<\/p>\n\n<h4>What you'll do \u2014 year one<\/h4>\n<p>Today, finance at Open lives in the founders' heads, a few spreadsheets, and our outsourced accounting partner. Your first major mission is to turn that into a structured, compounding system \u2014 a single source of truth on revenue, cash, burn, runway, unit economics, and pipeline that the leadership team trusts and acts on every week \u2014 and that the Series A leads will buy.<\/p>\n\n<p><strong>First 100 days<\/strong><\/p>\n<ul>\n<li>Listen tour: founders, every GTM leader, every customer-facing team. Map how money actually moves today.<\/li>\n<li>Stand up the weekly metrics email and the monthly board pack v1.<\/li>\n<li>Clean up the chart of accounts, compress the close to 5 business days.<\/li>\n<li>Build the v1 forecast model that survives contact with reality. Run it weekly.<\/li>\n<li>Pick the finance stack (NetSuite or QBO + Stripe\/Maxio + Brex\/Ramp\/Pleo + Pigment\/Cube\/Mosaic).<\/li>\n<li>Diagnose the unit economics: AI cost per resolved ticket, gross margin by segment, CAC payback by channel.<\/li>\n<\/ul>\n\n<p><strong>Months 4\u20139<\/strong><\/p>\n<ul>\n<li>Quarterback the Series A. Model, narrative, data room, diligence, term-sheet review.<\/li>\n<li>Stand up multi-entity consolidation cleanly (EU\/US subsidiaries).<\/li>\n<li>Build the pricing and deal-desk function \u2014 enterprise contract reviews, custom terms, ASC 606 compliance.<\/li>\n<li>Establish board cadence and investor reporting that the next round's leads will trust.<\/li>\n<\/ul>\n\n<p><strong>Months 10\u201318<\/strong><\/p>\n<ul>\n<li>Hit the targeted ARR WIG. Be the finance leader at the table for every enterprise close.<\/li>\n<li>Stand up audit-readiness in advance of Series B.<\/li>\n<li>Partner with CEO on geographic expansion math, M&A signals (acquihires, tuck-ins), and pricing 2.0.<\/li>\n<\/ul>\n\n<p><strong>Year 2 and beyond<\/strong><br>\nSeries B prep and close. Team scales to 7\u201312. Audit, tax, treasury, FX program.<\/p>\n\n<h4>Requirements<\/h4>\n<h4>What makes us excited about you<\/h4>\n<p>You're a player-coach, and you're proud of it.<br>\nYou're not above the spreadsheet. You can build the model on a Saturday and present it to a Sequoia partner on Monday. You're hiring your first report, but you're also the one who fixes the broken VLOOKUP at 9pm because the board pack ships tomorrow. You'll grow out of the spreadsheet work \u2014 but not for 12 months, and you're clear-eyed about that.<\/p>\n\n<p>You're a first-principles thinker who loves to build.<br>\nYou see chaos \u2014 disconnected systems, copy-pasted spreadsheets, inconsistent definitions of ARR \u2014 and your brain instantly starts drawing the clean version. You have strong opinions on what \"good\" looks like and you know how to get there one quarter at a time.<\/p>\n\n<p>You're commercial, not just operational.<br>\nYou're as comfortable in a pricing conversation as a close. You can read a usage-based contract and tell the AE what to push back on. You've sat across from a CRO and made them change their mind on a pricing tier with one chart.<\/p>\n\n<p>You're AI-native \u2014 and you can prove it.<br>\nPulling from Salesforce, Stripe, accounting platforms, billing, and spreadsheets doesn't intimidate you. SQL is a plus. But what we really care about: you've already wired AI into your finance workflow \u2014 Claude, MCP servers, Cube, Pigment, Mosaic, custom GPTs, agentic automations. You can show us a workflow you built last quarter that compressed a finance task by 5\u201310x. You read MCP server release notes for fun. When a new tool drops, you've tried it before your CEO sends you the link.<br>\nWe are an AI company. Our finance leader uses AI like our engineers do \u2014 as the default, not the exception. If \"I'm excited to learn about AI\" is your answer to this, you're not the right person for this seat.<\/p>\n\n<p>You care about outcomes, not output.<br>\nYou want to move charts and real numbers \u2014 runway extended, gross margin lifted, fundraise closed at a better valuation. You're comfortable designing the metric and proving impact.<\/p>\n\n<p>You can sit at any table.<br>\nYou can sit with our CEO, an enterprise CFO at a $5B payments company, a Sequoia partner, a Jordan-based engineer, and an SDR \u2014 and translate seamlessly between them.<\/p>\n\n<p>You're creative and unafraid to be wrong.<br>\nYou generate ideas constantly, test them quickly, and iterate without ego. You'll change your mind in public when the numbers say so.<\/p>\n\n<h4>Bonus<\/h4>\n<ul>\n<li>Experience at a YC-backed SaaS company, ideally infra\/AI\/CX.<\/li>\n<li>Multi-entity, multi-currency operating experience (especially MENA + EU + US).<\/li>\n<li>Lived through one IPO, acquisition, or unicorn round as part of the finance team.<\/li>\n<li>Network into top-tier Series A\/B investors who would back our next round.<\/li>\n<li>Understand usage-based or hybrid pricing models in B2B SaaS.<\/li>\n<li>You've used an MCP server, custom GPT, or Claude-driven workflow inside a finance org. We will ask to see it.<\/li>\n<li>You contribute publicly on AI-in-finance \u2014 posts, talks, GitHub, Substack \u2014 even casually.<\/li>\n<\/ul><\/p><p><\/p>

<p><h4>Description</h4> <p><strong>About Open</strong><br> Open.cx is the AI-native customer support platform built for enterprises that cannot afford slow, expensive, or mediocre support. We automate over 80% of interactions for companies like MoneyGram, Mollie, TicketSwap, and More.com handling voice, email, and web at massive scale with AI that thinks fast and feels human.</p> <p>We are backed by Y Combinator, Pioneer Fund, and X by Unifonic. We recently raised $7M+ and are accelerating from a startup into a global scale-up. Our customers replace entire stacks of legacy tools, cut support costs by up to 12×, and deliver dramatically better experiences to their own customers.</p> <p>We are a small, high-output team that ships fast, works directly with customers, and genuinely cares about the product we are building.</p> <h4>Your role</h4> <p>Open.cx is on a 3–5× ARR year, scaling from a handful of enterprise logos to category-contender status. We need a financial architect to build the foundation for that growth and stay with us all the way through the next decade of scale.</p> <p>Today, finance at Open lives in the founders' heads, a few spreadsheets, and an outsourced accounting partner. Your first mission is to turn that into a structured, compounding system—a single source of truth on revenue, cash, burn, runway, unit economics, and pipeline that the leadership team trusts and acts on every week and that Series A investors will be proud to sign off on.</p> <h4>What you'll do</h4> <p><strong>Year 1+</strong></p> <p><strong>First 100 days</strong></p> <ul> <li>Listen tour: founders, every GTM leader, every customer-facing team. Map how money actually moves today.</li> <li>Stand up the weekly metrics email and the monthly board pack version 1.</li> <li>Clean up the chart of accounts; compress the close to 5 business days.</li> <li>Build the version 1 forecast model that survives contact with reality. Run it weekly.</li> <li>Choose the finance stack (NetSuite or QBO + Stripe/Maxio + Brex/Ramp/Pleo + Pigment/Cube/Mosaic).</li> <li>Diagnose unit economics: AI cost per resolved ticket, gross margin by segment, CAC payback by channel.</li> </ul> <p><strong>Months 4–9</strong></p> <ul> <li>Quarterback the Series A: model, narrative, data room, diligence, term-sheet review.</li> <li>Stand up multi-entity consolidation cleanly (EU/US subsidiaries).</li> <li>Build the pricing and deal-desk function—enterprise contract reviews, custom terms, ASC 606 compliance.</li> <li>Establish board cadence and investor reporting that the next round's leads will trust.</li> </ul> <p><strong>Months 10–18</strong></p> <ul> <li>Hit the targeted ARR WIG. Be the finance leader at the table for every enterprise close.</li> <li>Stand up audit-readiness in advance of Series B.</li> <li>Partner with the CEO on geographic expansion, M&A signals, and pricing 2.0.</li> </ul> <p><strong>Year 2+</strong></p> <ul> <li>Series B prep and close. Finance team scales to 7–12. Audit, tax, treasury, FX programme.</li> </ul> <h4>Requirements</h4> <h4>What we're looking for</h4> <ul> <li>Player-coach mindset: you are not above the spreadsheet. You can build the model on a Saturday and present it to a Sequoia partner on Monday. You know you will grow out of this work but not for 12 months, and you are clear-eyed about that.</li> <li>First-principles thinker: you see chaos (disconnected systems, copy-pasted spreadsheets, inconsistent ARR definitions) and your brain draws the clean version.</li> <li>Commercially minded: you are as comfortable in a pricing conversation as in a close. You can read a usage-based contract and tell the AE what to push back on.</li> <li>AI-native: you have already wired AI into your finance workflow (Claude, MCP servers, Pigment, Mosaic, custom automations). You can show us a workflow you built that compressed a finance task by 5–10×. We are an AI company; our finance leader uses AI like our engineers do, as the default, not the exception.</li> <li>Outcomes-driven: you want to move real numbers: runway extended, gross margin lifted, fundraise closed at a better valuation.</li> <li>Sits at any table: you can translate seamlessly between our CEO, an enterprise CFO, a Sequoia partner, and an SDR.</li> </ul> <h4>Bonus points</h4> <ul> <li>Experience at a SaaS company, ideally in infrastructure, AI, or CX.</li> <li>Multi-entity, multi-currency operating experience (especially MENA + EU + US).</li> <li>Lived through one IPO, acquisition, or unicorn round as part of the finance team.</li> <li>Network into top-tier Series A/B investors who would back our next round.</li> <li>Experience with usage-based or hybrid pricing models in B2B SaaS.</li> <li>You contribute publicly on AI-in-finance (posts, talks, GitHub, Substack) even casually.</li> </ul> <h4>Benefits</h4> <h4>What we offer</h4> <ul> <li>YC-backed with real traction; we are in-market, growing, and trusted by enterprise names you know.</li> <li>Outsized career trajectory; early team members grow into leadership as we scale globally.</li> <li>Real ownership; your work will be seen, felt, and talked about company-wide.</li> <li>High-trust team; no politics, no bureaucracy; we move fast and support each other.</li> <li>Competitive compensation; top-of-market salary plus equity and full benefits.</li> </ul></p><p></p>

<p><h4>Description</h4> <p><strong>About Open</strong><br> Open.cx is the AI-native customer support platform built for enterprises that cannot afford slow, expensive, or mediocre support. We automate over 80% of interactions for companies like MoneyGram, Mollie, TicketSwap, and More.com handling voice, email, and web at massive scale with AI that thinks fast and feels human.</p> <p>We are backed by Y Combinator, Pioneer Fund, and X by Unifonic. We recently raised $7M+ and are accelerating from a startup into a global scale-up. Our customers replace entire stacks of legacy tools, cut support costs by up to 12×, and deliver dramatically better experiences to their own customers.</p> <p>We are a small, high-output team that ships fast, works directly with customers, and genuinely cares about the product we are building.</p> <h4>Your role</h4> <p>Open.cx is on a 3–5× ARR year, scaling from a handful of enterprise logos to category-contender status. We need a financial architect to build the foundation for that growth and stay with us all the way through the next decade of scale.</p> <p>Today, finance at Open lives in the founders' heads, a few spreadsheets, and an outsourced accounting partner. Your first mission is to turn that into a structured, compounding system—a single source of truth on revenue, cash, burn, runway, unit economics, and pipeline that the leadership team trusts and acts on every week and that Series A investors will be proud to sign off on.</p> <h4>What you'll do</h4> <p><strong>Year 1+</strong></p> <p><strong>First 100 days</strong></p> <ul> <li>Listen tour: founders, every GTM leader, every customer-facing team. Map how money actually moves today.</li> <li>Stand up the weekly metrics email and the monthly board pack version 1.</li> <li>Clean up the chart of accounts; compress the close to 5 business days.</li> <li>Build the version 1 forecast model that survives contact with reality. Run it weekly.</li> <li>Choose the finance stack (NetSuite or QBO + Stripe/Maxio + Brex/Ramp/Pleo + Pigment/Cube/Mosaic).</li> <li>Diagnose unit economics: AI cost per resolved ticket, gross margin by segment, CAC payback by channel.</li> </ul> <p><strong>Months 4–9</strong></p> <ul> <li>Quarterback the Series A: model, narrative, data room, diligence, term-sheet review.</li> <li>Stand up multi-entity consolidation cleanly (EU/US subsidiaries).</li> <li>Build the pricing and deal-desk function—enterprise contract reviews, custom terms, ASC 606 compliance.</li> <li>Establish board cadence and investor reporting that the next round's leads will trust.</li> </ul> <p><strong>Months 10–18</strong></p> <ul> <li>Hit the targeted ARR WIG. Be the finance leader at the table for every enterprise close.</li> <li>Stand up audit-readiness in advance of Series B.</li> <li>Partner with the CEO on geographic expansion, M&A signals, and pricing 2.0.</li> </ul> <p><strong>Year 2+</strong></p> <ul> <li>Series B prep and close. Finance team scales to 7–12. Audit, tax, treasury, FX programme.</li> </ul> <h4>Requirements</h4> <h4>What we're looking for</h4> <ul> <li>Player-coach mindset: you are not above the spreadsheet. You can build the model on a Saturday and present it to a Sequoia partner on Monday. You know you will grow out of this work but not for 12 months, and you are clear-eyed about that.</li> <li>First-principles thinker: you see chaos (disconnected systems, copy-pasted spreadsheets, inconsistent ARR definitions) and your brain draws the clean version.</li> <li>Commercially minded: you are as comfortable in a pricing conversation as in a close. You can read a usage-based contract and tell the AE what to push back on.</li> <li>AI-native: you have already wired AI into your finance workflow (Claude, MCP servers, Pigment, Mosaic, custom automations). You can show us a workflow you built that compressed a finance task by 5–10×. We are an AI company; our finance leader uses AI like our engineers do, as the default, not the exception.</li> <li>Outcomes-driven: you want to move real numbers: runway extended, gross margin lifted, fundraise closed at a better valuation.</li> <li>Sits at any table: you can translate seamlessly between our CEO, an enterprise CFO, a Sequoia partner, and an SDR.</li> </ul> <h4>Bonus points</h4> <ul> <li>Experience at a SaaS company, ideally in infrastructure, AI, or CX.</li> <li>Multi-entity, multi-currency operating experience (especially MENA + EU + US).</li> <li>Lived through one IPO, acquisition, or unicorn round as part of the finance team.</li> <li>Network into top-tier Series A/B investors who would back our next round.</li> <li>Experience with usage-based or hybrid pricing models in B2B SaaS.</li> <li>You contribute publicly on AI-in-finance (posts, talks, GitHub, Substack) even casually.</li> </ul> <h4>Benefits</h4> <h4>What we offer</h4> <ul> <li>YC-backed with real traction; we are in-market, growing, and trusted by enterprise names you know.</li> <li>Outsized career trajectory; early team members grow into leadership as we scale globally.</li> <li>Real ownership; your work will be seen, felt, and talked about company-wide.</li> <li>High-trust team; no politics, no bureaucracy; we move fast and support each other.</li> <li>Competitive compensation; top-of-market salary plus equity and full benefits.</li> </ul></p><p></p>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> Marketing Manager (Fashion Retail) <ul> <li><span>Posting Date</span>: 20/09/2026</li> <li><span>Ref</span>: SJOR-156</li> <li><span>Position</span>: Marketing Manager (Fashion Retail) </li> <li> </li><li><span>Location</span>: Jordan</li> <li><span>City</span>: Amman </li> <li> </li><li><span>Sector</span>: Retail</li> <li><span>Years of Experience</span> 6 to 8+ years of experience </li> <li> </li><li><span>Qualification</span>: Bachelor’s degree in Marketing, Business Administration, or a related field (MBA is an advantage)</li> <li><span>Salary</span>: 2300 JOD </li> <li><span>Workdays</span>: 5</li> <li> </li><li><span>Description</span>: Our client is a prominent, large-scale multi-brand retail and franchise operating group based in Amman, Jordan. The company is seeking an experienced, creative, and commercial Marketing Manager to own and execute the marketing, digital commerce, and brand-communications strategy across its multi-brand retail portfolio. The role reports directly to the Commercial Director and sits as a peer to the Brand Manager. The Marketing Manager will lead campaigns, e-commerce growth, CRM &amp; loyalty programs, public relations, and in-store visual merchandising (VM) standards, turning commercial goals and approved promotions into high-impact customer demand. Key focus areas include: <ul> <li> Developing and executing annual and seasonal multi-channel marketing calendars. </li> <li> Driving footfall, online sales conversion, brand awareness, and customer lifetime value. </li> <li> Managing e-commerce storefronts, digital marketing, and CRM/loyalty initiatives. </li> <li> Leading visual merchandising standards across all retail locations. </li> <li> Coordinating co-op marketing budgets and assets with international brand principals. </li> </ul> Key Responsibilities 1. Marketing Strategy, Campaigns &amp; Budget Management <ul> <li> Develop and execute annual/seasonal marketing calendars aligned with commercial targets and promotion schedules. </li> <li> Lead advertising, social media, influencer campaigns, PR, store launches, and mall partnerships. </li> <li> Own the consolidated marketing budget, track campaign ROI, footfall metrics, and cost per acquisition. </li> <li> Drive specific growth and recovery initiatives for underperforming brands within the portfolio. </li> </ul> 2. Digital Marketing &amp; E-Commerce Storefront <ul> <li> Oversee digital marketing activities across web, social, and paid media while maintaining franchisor brand voice. </li> <li> Own online product presentation, merchandising, customer user journey, and conversion rate optimization. </li> <li> Collaborate with IT on platform reliability, Operations on order fulfillment, and Brand Managers on stock alignment. </li> </ul> 3. CRM, Loyalty &amp; Customer Insights <ul> <li> Manage customer databases, loyalty programs, and personalized CRM/WhatsApp marketing campaigns. </li> <li> Drive repeat-purchase rates and customer lifetime value (LTV). </li> <li> Monitor consumer behavior, shopping trends, and competitor marketing activities to refine brand positioning. </li> </ul> 4. Visual Merchandising (VM) &amp; In-Store Experience <ul> <li> Establish and oversee in-store visual merchandising and display standards in compliance with brand guidelines. </li> <li> Lead the VM team in executing window displays, store campaign setups, and compliance audits. </li> <li> Partner with Area and Store Managers to maintain high day-to-day VM upkeep and store readiness. </li> </ul> 5. Franchisor Marketing Relations &amp; Agency Leadership <ul> <li> Act as the primary counterpart for brand principals' marketing teams and adapt global assets for local execution. </li> <li> Plan and execute co-op marketing fund spend and submit performance proofs to franchisors. </li> <li> Manage external creative, media-buying, PR agencies, and evaluate direct report performance (Digital Coordinator &amp; VM team). </li> <li> Prepare and present monthly marketing dashboards (ROI, footfall, CRM growth, VM compliance) to executive leadership. </li> </ul> Key Qualifications <ul> <li> Experience: 6 to 8+ years of marketing leadership experience, ideally within multi-brand retail, fashion, or franchise operations. </li> <li> Education: Bachelor’s degree in Marketing, Business Administration, or a related field (MBA preferred). </li> <li> Technical &amp; Functional Expertise: <ul> <li> Proven track record in digital marketing, e-commerce growth, and CRM/loyalty execution. </li> <li> Solid background in overseeing visual merchandising (VM) or working under strict international franchisor guidelines. </li> <li> Experience operating within global franchisor brand guidelines. </li> <li> Strong skills in marketing budget ownership, ROI analytics, and external agency management. </li> </ul> </li> <li> Market Knowledge: Familiarity with the Jordanian / Levant retail market is preferred. </li> <li> Languages: Native-level Arabic and fluent English language skills are mandatory. </li> <li> Location: Based in Amman, Jordan. </li> </ul> <br> More<br> </li> </ul> <br> <br> </div>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>A leading global pharmaceutical company is seeking a Director, Data &amp; Analytics to take ownership of the strategy, architecture, delivery, and operational performance of its Data &amp; Analytics function. This is a hands-on leadership role overseeing the enterprise data platform, the data products the business relies on, and the team behind them, setting technical direction, delivery standards, and investment priorities while staying personally engaged in key architecture and design decisions. The ideal candidate combines strong technical credibility with senior leadership experience, and will be directly accountable for making the platform AI-ready, ensuring data is governed, traceable, and model-grade to support regulated AI deployment.</p><p>Responsibilities:</p><p>1. Platform Architecture &amp; Engineering Leadership:</p><ul><li>Own the enterprise data platform, Azure Data Lake Storage Gen2, Databricks lakehouse (Medallion architecture), Power BI, and Unity Catalog, ensuring it is architecturally sound, standardised, reliable, and engineered to scale</li><li>Set and enforce platform engineering standards: ingestion patterns, transformation conventions, Medallion layer contracts, data quality gates, Bronze-to-Gold promotion criteria, and the CI/CD framework that delivers all of it</li><li>Own the Unity Catalog governance model, RBAC, lineage, business glossary, and metric definitions, as the platform-enforced foundation for trusted data</li><li>Drive the platform roadmap from current state to target, sequencing technical debt remediation, new capability build-out, and platform readiness for downstream AI and analytics demand</li><li>Personally lead design reviews and architecture decisions for the platform, engaging directly with the engineering team on complex technical problems where senior technical judgment is required</li><li>Own data platform observability and operational excellence, pipeline reliability, SLA adherence, incident response, and data quality monitoring</li></ul><p>2. Delivery &amp; Data Products:</p><ul><li>Run the D&amp;A delivery programme, from source ingestion and pipeline engineering through to analytics, semantic layer, and data product delivery for business functions</li><li>Set and enforce delivery standards: sprint cadence, code review, documentation, testing, validation, and release management practices that govern all team output</li><li>Define and own data SLAs to the business, pipeline refresh frequency, availability, and incident response commitments, and ensure delivery is held against them</li><li>Own the data integration roadmap, prioritising ingestion of new source systems into the lake in alignment with business demand and platform readiness</li><li>Manage complex integration challenges across source systems, engaging directly on technical constraints, working with upstream owners, and designing solutions that align data latency and refresh frequency with business needs</li><li>Own the portfolio of data products, datasets, semantic models, and analytics deliverables, ensuring each is documented, well-understood, and fit for the business question it answers</li><li>Define and own the standard for what a Gold-layer data product must satisfy to be AI-ready, distinguishing analytics-grade from model-training-grade, with explicit criteria covering completeness, label integrity, statistical consistency, and lineage traceability</li></ul><p>3. Data Governance &amp; Quality:</p><ul><li>Design and operate the data governance operating model, data ownership, stewardship, quality standards, business glossary, and metric definitions</li><li>Embed data quality gates into Medallion layer promotion, making quality a precondition of Bronze-to-Gold progression, with measurable thresholds and clear remediation paths</li><li>Own data lineage visibility across the platform so business users can trace the provenance of every number they rely on, end-to-end from source to dashboard</li><li>Lead the technical evaluation, selection, and implementation of enterprise data catalogue tooling, including integration with Unity Catalog and the platform metadata layer</li><li>Embed appropriate data handling, validation, and audit-trail practices for regulated data domains, partnering with Quality, Regulatory, and Legal to ensure compliance requirements are met by design</li><li>Establish data quality measurement as a managed practice, KPIs, dashboards, periodic review, and accountability with data owners</li><li>Establish data lineage and provenance practices that satisfy AI explainability and regulatory auditability requirements, including GxP-compliant audit trails for Quality, Manufacturing, and Regulatory AI use cases</li></ul><p>4. Team Leadership &amp; Capability Development:</p><ul><li>Lead, hire, and develop the D&amp;A team, Data Engineers, Analytics Engineers, BI Developers, and Business Analysts, sequencing hires in alignment with the platform roadmap and delivery demand</li><li>Define and evolve the role design, skills profile, and career framework for the team within the broader Data &amp; AI CoE structure</li><li>Set the team's performance culture: clear ownership, high engineering standards, fast feedback, continuous learning, and documentation as a team discipline</li><li>Coach and develop team members directly, identifying high-potential individuals, investing in their technical and leadership growth, and building bench strength across roles</li><li>Manage team capacity and allocation across the platform roadmap and business delivery demand, ensuring focus stays on high-value work aligned to strategic priorities</li><li>Set the engineering craft culture, code review, design review, pairing, and shared technical standards, that lifts the technical quality of all team output</li></ul></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><p>Bachelor's degree in Computer Science, Information Systems, Data Engineering, Mathematics, or related discipline</p><p>Master's degree in Data Science, Computer Science, Business Administration, or related field is preferred</p><p>Relevant certifications in cloud data platforms (e.g., Azure Data Engineer, Databricks Certified Data Engineer Professional) are preferred</p><p>Minimum 10 years of progressive experience in data engineering, data platform, or enterprise analytics roles</p><p>Minimum 5 years in a senior leadership role with direct team management accountability, hiring, developing, and performance managing a multi-disciplinary technical team</p><p>Proven hands-on production experience with Databricks (Delta Lake, Unity Catalog) and Azure data services at platform-design and engineering-lead level</p><p>Proven track record building or substantially remediating a cloud-native data platform in a complex, multi-source enterprise environment</p><p>Experience with Medallion/lakehouse architecture patterns in production</p><p>Experience designing data products and platform capabilities for AI/ML consumption, including Feature Store design, training dataset engineering, and ML data lineage is preferred</p><p>Experience working at the interface of a data platform team and an AI/ML team, translating model requirements into data infrastructure specifications and owning the data readiness handoff is preferred</p><p>Experience leading a data governance or catalogue implementation, from design through to business adoption</p><p>Life sciences, pharmaceutical, or other regulated industry experience is preferred</p><p>Skills :</p><p>Technical Competencies:</p><ul><li>Data Platform Architecture</li><li>Data Engineering, Pipeline Design &amp; CDC Patterns</li><li>Data Modelling, Transformation &amp; Engineering Standards</li><li>Data Governance, Quality &amp; Catalogue</li><li>Analytics &amp; BI Delivery</li><li>MLOps &amp; ML Platform Foundations</li><li>Delivery Management &amp; Agile Methods</li></ul><p>Platform &amp; Technical Skills:</p><ul><li>Deep practical expertise in Azure data services: ADLS Gen2, Azure Data Factory, Azure DevOps</li><li>Hands-on production experience with Databricks, Delta Lake, notebooks, Unity Catalog, MLflow</li><li>Strong understanding of incremental load and CDC patterns across enterprise source systems (SAP, Veeva, SuccessFactors, and similar)</li><li>Power BI at the semantic layer level, understanding how the semantic layer should be designed for enterprise scale</li><li>CI/CD for data pipelines, practical implementation at production scale</li><li>Data quality frameworks, profiling, expectation testing, alerting, and remediation workflows</li><li>Working knowledge of modern data governance tooling: Unity Catalog, Collibra, Purview, or DataHub</li></ul><p>Leadership &amp; Business Skills:</p><ul><li>Credible with both technical teams and senior business stakeholders</li><li>Strong delivery discipline, owns commitments, communicates risks early, and sizes work realistically</li><li>Structured thinker, able to take a complex current state and produce a clear, prioritised, sequenced roadmap</li><li>Strong written and verbal communication in English; Arabic proficiency valued</li><li>Cultural intelligence for working effectively across MENA, US, and Europe</li></ul><p></p></section>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br>What this opportunity involves: We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT: Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for: 8+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard: Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidEffort estimate Tasks for this project are estimated to take 30 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation: Up to $150/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~30 hours each; you set your own schedule.<br></span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for 5+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Effort estimate Tasks for this project are estimated to take 20 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation Up to $50/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~20 hours each; you set your own schedule.<br></span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for 5+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Effort estimate Tasks for this project are estimated to take 20 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation Up to $50/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~20 hours each; you set your own schedule.<br></span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for 5+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Effort estimate Tasks for this project are estimated to take 20 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation Up to $50/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~20 hours each; you set your own schedule.<br></span> </div>

<p><h4>Description<\/h4>\n<p>Please submit your CV in English and indicate your level of English proficiency.<\/p>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n<li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n<li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n<li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n<li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n<li>Not data labeling<\/li>\n<li>Not prompt engineering<\/li>\n<li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n<li>5+ years in software development<\/li>\n<li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n<li>Experience writing tests (functional, integration)<\/li>\n<li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply ? Pass qualification(s) ? Join a project ? Complete tasks ? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $50\/hr equivalent, depending on level and pace. Tasks are estimated at ~20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Description<\/h4>\n<p>Please submit your CV in English and indicate your level of English proficiency.<\/p>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n<li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n<li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n<li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n<li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n<li>Not data labeling<\/li>\n<li>Not prompt engineering<\/li>\n<li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n<li>5+ years in software development<\/li>\n<li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n<li>Experience writing tests (functional, integration)<\/li>\n<li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply ? Pass qualification(s) ? Join a project ? Complete tasks ? Get paid<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $50\/hr equivalent, depending on level and pace. Tasks are estimated at ~20 hours each; you set your own schedule.<\/p><\/p><p><\/p>