Medical Representative Jobs in Jordan 2026 - Apply Now

Full Time 0 to 0 years Gulf Talnet

AI Solution Architect

On-site - Jordan - Amman 21 July 2026

Information Technology

<h4>Description</h4> A leading global pharmaceutical company is seeking an AI solutions architect who is responsible for designing, governing, delivering, and evolving the enterprise AI technical architecture across the company. Acting as the technical authority and delivery lead for AI within the AI Centre of Excellence, this role defines architectural standards, evaluates and governs AI platforms and solutions, prioritising off-the-shelf and vendor-led approaches, and ensures that all AI initiatives are scalable, secure, compliant, and aligned with the company's digital transformation strategy. The AI solutions architect works closely with AI engineers, IT business partners, AI champions, and business function leaders to assess technical feasibility, lead end-to-end solution delivery, support AI proof of value (POV) execution, and translate strategic AI ambitions into sound, future-proof solutions, ensuring every initiative is delivered on a technically rigorous and governance-compliant foundation. <h4>Responsibilities:</h4> <ol> <li>Strategy & architecture leadership Define and own the enterprise AI technical architecture framework, including reference architectures, design patterns, integration blueprints, and technology standards across cloud and on-premises environments. Collaborate with the architecture advisory board to ensure the AI architecture roadmap is aligned with the company's overall digital transformation strategy and enterprise IT architecture principles. Evaluate emerging AI technologies, frameworks, and platforms, providing technical direction and recommendations to senior IT and business stakeholders. Lead architectural governance for all AI initiatives, ensuring solutions adhere to approved standards, security requirements, regulatory constraints, and scalability principles. Define and maintain the enterprise AI technology stack, including cloud AI services (Azure, AWS, GCP), MLOps platforms, data platforms, and integration middleware.</li> <li>AI solution design & technical oversight Architect end-to-end AI solutions across the company's key business domains including manufacturing, quality & regulatory affairs, R&D, commercial, supply chain, HR, and finance. Produce high-quality architecture deliverables including solution design documents, architecture decision records (ADRs), technical specifications, data flow diagrams, and integration architecture blueprints. Lead technical design reviews and architecture assessments for all AI initiatives in the portfolio, ensuring fitness for purpose, scalability, and compliance. Define AI integration patterns with enterprise systems including SAP, MES, LIMS, CRM, and M365 ecosystems, ensuring seamless interoperability. Guide AI developers in translating architecture designs into well-structured, maintainable, and production-ready solutions. Oversee the design of MLOps pipelines including model training, validation, deployment, monitoring, and retraining workflows.</li> <li>Platform engineering & infrastructure Own the architecture and governance of enterprise AI platforms including Microsoft Azure AI, Azure Machine Learning, Microsoft 365 Copilot, and other approved AI tooling. Define cloud infrastructure architecture for AI workloads including compute, storage, networking, and security configurations aligned with the company IT standards. Establish standards for model lifecycle management including versioning, registry, performance monitoring, drift detection, and retraining triggers. Drive the design of data architecture components critical to AI including feature stores, data lakes, vector databases, and real-time data pipelines in collaboration with the Data & Analytics team. Ensure AI platforms meet GxP validation, 21 CFR Part 11, and audit trail requirements where applicable.</li> <li>Governance, compliance & responsible AI Embed regulatory and compliance requirements, including FDA AI/ML guidance, EMA requirements, GxP, GDPR, and HIPAA, into AI architecture design and review processes. Define and enforce responsible AI architectural guardrails including model explainability, bias detection, fairness assessments, and human-in-the-loop design patterns. Maintain AI architecture governance documentation including standards, patterns, approved toolsets, and deviation processes within the enterprise AI knowledge repository. Coordinate with IT security, data privacy, legal, quality assurance, and regulatory affairs to ensure AI solutions meet all applicable oversight requirements. Support E-AIAB governance processes by providing technical input into initiative assessments, vendor evaluations, and POV planning.</li> <li>Technical leadership & enablement Provide technical mentorship, code and architecture reviews, and hands-on guidance to the AI developer team. Define engineering best practices, coding standards, and DevOps/MLOps conventions for the AI team. Collaborate with external vendors, implementation partners, and cloud providers to assess solutions, conduct technical due diligence, and ensure delivery quality. Contribute technical expertise to vendor RFP/RFI processes, proof-of-concept evaluations, and contract assessments. Represent the company's AI technical standards in cross-functional project delivery teams and steering committees.</li> <li>Stakeholder engagement & communication Translate complex technical architecture concepts into clear, accessible language for business stakeholders, executive leadership, and non-technical audiences. Serve as the primary technical escalation point for AI platform issues, architecture deviations, and integration challenges. Collaborate with IT business partners and AI champions to provide technical feasibility input into AI opportunity assessments. Participate in external pharmaceutical AI forums, cloud provider events, and technology conferences to maintain leading-edge awareness and contribute to the company's technical reputation.</li> </ol> <h4>Requirements</h4> Bachelor's degree in computer science, information technology, software engineering, data science, or related technical field. Master's degree in artificial intelligence, data science, computer science, or related discipline is preferred. Microsoft Azure Solutions Architect Expert, Azure AI Engineer Associate, or equivalent cloud architecture certification is preferred. TOGAF or equivalent enterprise architecture certification is preferred. 7–10 years of professional experience in IT, software engineering, data & analytics, or AI/ML implementation. 4–6 years of hands-on experience designing and delivering AI/ML solutions on cloud platforms (Azure, AWS, or GCP) in a production environment. Proven track record of owning end-to-end AI solution architecture in a complex, cross-functional enterprise environment. Experience with Microsoft Azure AI, Azure Machine Learning, and Microsoft 365 Copilot architecture and deployment is preferred. Pharmaceutical, healthcare, life sciences, or other regulated industry experience is preferred. Experience with GxP validation, 21 CFR Part 11, or regulatory technology compliance in an AI/ML context is preferred. <h4>Skills:</h4> Technical competencies: AI/ML architecture & solution design - cloud platform architecture (Azure / AWS / GCP) - ML ops & model lifecycle management - enterprise integration & API design - data architecture & data engineering - AI governance, ethics & responsible AI - pharmaceutical regulatory compliance (GxP, FDA, EMA) AI & technology skills: Deep expertise in AI/ML architecture patterns, including supervised/unsupervised learning, NLP, computer vision, generative AI, and LLM-based solution design. Strong hands-on proficiency with Azure AI services, Azure Machine Learning, MLflow, or equivalent MLOps tooling. Solid experience designing and deploying generative AI solutions including RAG architectures, LLM orchestration (LangChain, Semantic Kernel), and enterprise copilot patterns. Strong command of enterprise integration architecture including REST APIs, event-driven architecture, message queues, and middleware platforms. Proficiency in cloud infrastructure design including IaC (Terraform, Bicep), containerization (Docker, Kubernetes), and CI/CD pipelines. Strong understanding of data architecture components including data lakes, lakehouses, feature stores, and vector databases. Working knowledge of pharmaceutical business processes including GxP operations, quality management systems, and regulatory affairs workflows (preferred). Solid understanding of AI governance frameworks, responsible AI principles, data privacy regulations (GDPR, HIPAA), and IT security principles relevant to AI deployment.

Full Time 0 to 0 years Gulf Talnet

AI Solution Architect

On-site - Jordan - Amman 21 July 2026

Information Technology

<h4>Description</h4> A leading global pharmaceutical company is seeking an AI solutions architect who is responsible for designing, governing, delivering, and evolving the enterprise AI technical architecture across the company. Acting as the technical authority and delivery lead for AI within the AI Centre of Excellence, this role defines architectural standards, evaluates and governs AI platforms and solutions, prioritising off-the-shelf and vendor-led approaches, and ensures that all AI initiatives are scalable, secure, compliant, and aligned with the company's digital transformation strategy. The AI solutions architect works closely with AI engineers, IT business partners, AI champions, and business function leaders to assess technical feasibility, lead end-to-end solution delivery, support AI proof of value (POV) execution, and translate strategic AI ambitions into sound, future-proof solutions, ensuring every initiative is delivered on a technically rigorous and governance-compliant foundation. <h4>Responsibilities:</h4> <ol> <li>Strategy & architecture leadership Define and own the enterprise AI technical architecture framework, including reference architectures, design patterns, integration blueprints, and technology standards across cloud and on-premises environments. Collaborate with the architecture advisory board to ensure the AI architecture roadmap is aligned with the company's overall digital transformation strategy and enterprise IT architecture principles. Evaluate emerging AI technologies, frameworks, and platforms, providing technical direction and recommendations to senior IT and business stakeholders. Lead architectural governance for all AI initiatives, ensuring solutions adhere to approved standards, security requirements, regulatory constraints, and scalability principles. Define and maintain the enterprise AI technology stack, including cloud AI services (Azure, AWS, GCP), MLOps platforms, data platforms, and integration middleware.</li> <li>AI solution design & technical oversight Architect end-to-end AI solutions across the company's key business domains including manufacturing, quality & regulatory affairs, R&D, commercial, supply chain, HR, and finance. Produce high-quality architecture deliverables including solution design documents, architecture decision records (ADRs), technical specifications, data flow diagrams, and integration architecture blueprints. Lead technical design reviews and architecture assessments for all AI initiatives in the portfolio, ensuring fitness for purpose, scalability, and compliance. Define AI integration patterns with enterprise systems including SAP, MES, LIMS, CRM, and M365 ecosystems, ensuring seamless interoperability. Guide AI developers in translating architecture designs into well-structured, maintainable, and production-ready solutions. Oversee the design of MLOps pipelines including model training, validation, deployment, monitoring, and retraining workflows.</li> <li>Platform engineering & infrastructure Own the architecture and governance of enterprise AI platforms including Microsoft Azure AI, Azure Machine Learning, Microsoft 365 Copilot, and other approved AI tooling. Define cloud infrastructure architecture for AI workloads including compute, storage, networking, and security configurations aligned with the company IT standards. Establish standards for model lifecycle management including versioning, registry, performance monitoring, drift detection, and retraining triggers. Drive the design of data architecture components critical to AI including feature stores, data lakes, vector databases, and real-time data pipelines in collaboration with the Data & Analytics team. Ensure AI platforms meet GxP validation, 21 CFR Part 11, and audit trail requirements where applicable.</li> <li>Governance, compliance & responsible AI Embed regulatory and compliance requirements, including FDA AI/ML guidance, EMA requirements, GxP, GDPR, and HIPAA, into AI architecture design and review processes. Define and enforce responsible AI architectural guardrails including model explainability, bias detection, fairness assessments, and human-in-the-loop design patterns. Maintain AI architecture governance documentation including standards, patterns, approved toolsets, and deviation processes within the enterprise AI knowledge repository. Coordinate with IT security, data privacy, legal, quality assurance, and regulatory affairs to ensure AI solutions meet all applicable oversight requirements. Support E-AIAB governance processes by providing technical input into initiative assessments, vendor evaluations, and POV planning.</li> <li>Technical leadership & enablement Provide technical mentorship, code and architecture reviews, and hands-on guidance to the AI developer team. Define engineering best practices, coding standards, and DevOps/MLOps conventions for the AI team. Collaborate with external vendors, implementation partners, and cloud providers to assess solutions, conduct technical due diligence, and ensure delivery quality. Contribute technical expertise to vendor RFP/RFI processes, proof-of-concept evaluations, and contract assessments. Represent the company's AI technical standards in cross-functional project delivery teams and steering committees.</li> <li>Stakeholder engagement & communication Translate complex technical architecture concepts into clear, accessible language for business stakeholders, executive leadership, and non-technical audiences. Serve as the primary technical escalation point for AI platform issues, architecture deviations, and integration challenges. Collaborate with IT business partners and AI champions to provide technical feasibility input into AI opportunity assessments. Participate in external pharmaceutical AI forums, cloud provider events, and technology conferences to maintain leading-edge awareness and contribute to the company's technical reputation.</li> </ol> <h4>Requirements</h4> Bachelor's degree in computer science, information technology, software engineering, data science, or related technical field. Master's degree in artificial intelligence, data science, computer science, or related discipline is preferred. Microsoft Azure Solutions Architect Expert, Azure AI Engineer Associate, or equivalent cloud architecture certification is preferred. TOGAF or equivalent enterprise architecture certification is preferred. 7–10 years of professional experience in IT, software engineering, data & analytics, or AI/ML implementation. 4–6 years of hands-on experience designing and delivering AI/ML solutions on cloud platforms (Azure, AWS, or GCP) in a production environment. Proven track record of owning end-to-end AI solution architecture in a complex, cross-functional enterprise environment. Experience with Microsoft Azure AI, Azure Machine Learning, and Microsoft 365 Copilot architecture and deployment is preferred. Pharmaceutical, healthcare, life sciences, or other regulated industry experience is preferred. Experience with GxP validation, 21 CFR Part 11, or regulatory technology compliance in an AI/ML context is preferred. <h4>Skills:</h4> Technical competencies: AI/ML architecture & solution design - cloud platform architecture (Azure / AWS / GCP) - ML ops & model lifecycle management - enterprise integration & API design - data architecture & data engineering - AI governance, ethics & responsible AI - pharmaceutical regulatory compliance (GxP, FDA, EMA) AI & technology skills: Deep expertise in AI/ML architecture patterns, including supervised/unsupervised learning, NLP, computer vision, generative AI, and LLM-based solution design. Strong hands-on proficiency with Azure AI services, Azure Machine Learning, MLflow, or equivalent MLOps tooling. Solid experience designing and deploying generative AI solutions including RAG architectures, LLM orchestration (LangChain, Semantic Kernel), and enterprise copilot patterns. Strong command of enterprise integration architecture including REST APIs, event-driven architecture, message queues, and middleware platforms. Proficiency in cloud infrastructure design including IaC (Terraform, Bicep), containerization (Docker, Kubernetes), and CI/CD pipelines. Strong understanding of data architecture components including data lakes, lakehouses, feature stores, and vector databases. Working knowledge of pharmaceutical business processes including GxP operations, quality management systems, and regulatory affairs workflows (preferred). Solid understanding of AI governance frameworks, responsible AI principles, data privacy regulations (GDPR, HIPAA), and IT security principles relevant to AI deployment.

0 to 0 years Bayt

Artificial Intelligence Analyst

On-site - Jordan 3 July 2026

Other

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> Tiered Approach In line with the commitment to safeguard capacity and support personnel already in the Organization, a majority of UNDP UNCDF/UNV vacancies are advertised using a tiered application process whereby: <ul> <li>Tier 0: UNDP/UNCDF/UNV IP staff holding permanent (PA) and fixed-term (FTA) appointments, whose posts will be abolished, or contracts will be terminated or not renewed during 2026.</li> <li>Tier 1: Other UNDP/UNCDF/UNV staff holding permanent (PA) and fixed-term (FTA) appointments</li> <li>Tier 2: UNDP/UNCDF/UNV staff holding temporary appointments (TA), personnel on regular PSA contracts, and Expert and Specialist UN Volunteers</li> <li>Tier 3 or no tier indicated: All other contract types from UNDP/UNCDF/UNV and other agencies, and other external candidates</li> </ul> Please make note of the Tier(s) indicated in the vacancy title, if any, and ensure that you satisfy the eligibility to apply. Background Anchored in the 2030 Agenda for Sustainable Development and aligned with the UNDP Strategic Plan 2026–2029, the Regional Bureau for Arab States (RBAS) supports the Arab States region in achieving sustainable development by eradicating poverty in all its forms, accelerating structural transformations, and building resilience to crises and shocks. RBAS operates across the full spectrum of development settings in the region, including Least Developed Countries, Middle Income Countries, Net Contributor Countries, and countries affected by crisis.  The Regional Hub for Arab States in Amman serves as the centre for thought leadership, crisis response, and country office support. Its work spans strategic policy advice, programme development and implementation, capacity development, policy research and analysis, knowledge services, and operations support. The Amman Regional Hub hosts the Regional Programme for Arab States, which responds to emerging regional priorities through knowledge generation, regional initiatives, and development projects aligned with the Regional Programme Document 2026–2029.  Artificial Intelligence has emerged as a defining force in the development landscape of the Arab States region, presenting both transformative opportunities and significant governance challenges. In response to growing demand from governments, donors, civil society, and private sector partners, RBAS is accelerating its engagement on AI as a cross-cutting enabler for sustainable development outcomes. This work is embedded within the Regional Programme and implemented in close collaboration with governance, innovation, and programme teams across the Hub.  UNDP’s AI Sprint is a flagship initiative aimed at accelerating the adoption of Artificial Intelligence for sustainable development across the Arab States and Africa. The Sprint is structured around four key pillars: AI Landscape Assessment (AILA), AI Trust & Safety, AI Capacity Building, and AI Model Development. The initiative seeks to equip governments, the private sector, academia, and civil society with the skills, policies, and tools required to leverage AI responsibly and effectively in support of the Sustainable Development Goals (SDGs). Through the AI Sprint, UNDP supports partner countries in identifying national AI priorities, assessing ecosystem readiness, strengthening governance frameworks, building human capacities, and advancing inclusive AI solutions particularly in Arabic and local languages.  Within this framework, and under the direct supervision of the head or regional digital and AI team - Digital Transformation Specialist the Artificial Intelligence(AI )Analyst will be responsible of supporting the implementation of activities across the four AI Sprint pillars, while also providing substantive support to the Regional Programme’s AI-related components and delivering tailored AI technical support to UNDP Country Offices across the Arab States. The candidate will support research, policy development, and knowledge sharing to ensure that AI adoption in the region is ethical, inclusive, and aligned with global best practices.  Duties and Responsibilities Under the direct supervision of the Digital Transformation Specialist, the AI Analyst will perform the following key functions:  1. Contribute to the Regional AI work  <ul> <li>Providing substantive support to the Regional Programme’s AI-related components. </li> <li>Delivering tailored AI technical support to UNDP Country Offices across the Arab States.</li> <li>Coordinate  AI Landscape Assessment (AILA) </li> <li>Coordinate country-level assessments of AI readiness, ecosystem maturity, and policy frameworks. </li> <li>Analyze findings to produce synthesis reports and actionable insights for national and regional planning. </li> <li>Support the facilitation of engagement with governments, academia, and private sector partners to validate and apply assessment results. </li> </ul> 2. Contribute to AI Trust & Safety  <ul> <li>Support the development of responsible AI governance frameworks and practical guidance on fairness, accountability, transparency, and privacy. </li> <li>Develop tools and methodologies for risk assessment and mitigation. </li> <li>Track global AI governance trends and align regional efforts with international standards. </li> </ul> 3.Facilitate AI Capacity Building  <ul> <li>Design and coordinate capacity-building programmes and workshops for policymakers, practitioners, and institutions. </li> <li>Support in developing learning materials and toolkits on AI ethics, governance, and applications for sustainable development. </li> <li>Support the coordination with UNDP Country Offices to identify AI priorities, technical support needs, and opportunities for south-south collaboration. </li> </ul> 4.Support AI model development and knowledge sharing  <ul> <li>Support initiatives focused on Arabic and local-language AI model development and deployment. </li> <li>Support the engagement with partners to enhance linguistic inclusivity and representation in AI systems. </li> <li>Document lessons learned and promote inclusive and culturally relevant AI approaches. </li> </ul> 5.Research, policy and partnership support  <ul> <li>Prepare policy briefs, knowledge products, and concept notes on responsible AI. </li> <li>Support coordination among partners, including governments, research institutions, and the private sector. </li> <li>Contribute to advocacy, communication, and reporting on AI Sprint outcomes and progress. </li> </ul> 6. The incumbent performs other duties within their functional profile as deemed necessary for the efficient functioning of the Office and the Organization.  Institutional Arrangement  The Artificial Intelligence (AI) Analyst will report directly to the Digital Transformation Specialistand work in close collaboration with:  • The Regional AI and Digital Team in Amman.  • The AI, Digital, and Innovation Hub in New York.  • Regional Programme Team.  • UNDP Country Offices across the Arab States.  The candidate will work remotely but may be required to travel for regional workshops, consultations, and coordination meetings. The candidate will provide their own work equipment (laptop, internet connection, etc.) and maintain flexibility to work across time zones.  Competencies Core Competencies Achieve Results – LEVEL 1: Plans and monitors own work, pays attention to details, delivers quality work by deadline. Think Innovatively – LEVEL 1: Open to creative ideas/known risks, is a pragmatic problem solver, makes improvements. Learn Continuously – LEVEL 1: Open-minded and curious, shares knowledge, learns from mistakes, asks for feedback. Adapt with Agility – LEVEL 1: Adapts to change, constructively handles ambiguity/uncertainty, is flexible. Act with Determination – LEVEL 1: Shows drive and motivation, able to deliver calmly in the face of adversity, confident. Engage and Partner – LEVEL 1: Demonstrates compassion and understanding towards others, forms positive relationships. Enable Diversity and Inclusion – LEVEL 1: Appreciates and respects differences, is aware of unconscious bias, confronts discrimination. Cross-Functional & Technical Competencies Digital <ul> <li>Artificial Intelligence Thought Leadership Deep understanding of current and emerging trends in AI, along with their social, ethical, and economic implications, including data privacy and human rights. Ability to engage with industry experts, government representatives, and academics to stay updated on the latest trends and to form partnerships. Strong skills in conducting and interpreting research to support thought leadership initiatives, including white papers, policy briefs, or case studies. Understanding of global trends and cultural nuances as they relate to the field of AI. Familiarity with international laws and standards regarding technology, particularly in a multi-stakeholder environment like the UN.</li> <li>Policy/Regulations for Digital and Emerging Technology Ability to design or advise on regulations and policy for digital and emerging technology.</li> <li>Transformation in Developing Organizations Knowledge of re-designing processes and leading projects that involve development issues.</li> </ul> Data <ul> <li>Data Analysis Ability to extract, analyze and visualize data to form meaningful insights and aid effective business decision-making.</li> </ul> Business Management <ul> <li>Digital Awareness and Literacy Ability to monitor new and emerging technologies, as well as understand their usage, potential, limitations, impact, and added value. Ability to rapidly and readily adopt and use new technologies in professional activities, and to empower others to do the same.</li> </ul> Communication <ul> <li>Digital Capacity Building Ability to increase the impact of digital communications through trainings, partnerships, and relevant resources.</li> </ul> Business Direction & Strategy <ul> <li>Systems Thinking Ability to use objective problem analysis and judgement to understand how interrelated elements coexist within an overall process or system, and to consider how altering one element can impact on other parts of the system.</li> </ul> *Please refer to the competency framework site for the entire list of competencies and further explanations. Required Skills and Experience Minimum Education Requirements <ul> <li>An advanced university degree (Master’s degree or equivalent) in Artificial Intelligence, Computer Science, Data Science, Telecommunications, International Development, Public Policy, or a related field is required.</li> </ul> OR <ul> <li>A first-level university degree (Bachelor’s degree) in the areas stated above, in combination with an additional two years of qualifying experience, will be given due consideration in lieu of the advanced university degree.</li> </ul> Minimum Years of Relevant Work Experience <ul> <li>Minimum of 2 years (with a Master’s degree) or 4 years (with a Bachelor’s degree) of progressively responsible professional experience in AI development, digital innovation, or applied data technologies.</li> </ul> Required Skills <ul> <li>Proven experience in conducting AI ecosystem assessments and policy design.</li> <li>Demonstrated experience in AI capacity building, research, and stakeholder engagement.</li> <li>Experience in developing AI models, AI governance, responsible AI, digital transformation, and emerging technology frameworks.</li> <li>Proven experience in developing project proposals and partnership engagement.</li> <li>Demonstrated ability to draft high-quality technical reports, policy briefs, concept notes, and strategic recommendations.</li> </ul> Desired Skills <ul> <li>Experience in international or regional AI or digital development projects in the Arab States Region is an asset.</li> <li>Familiarity with UN or other international development organizations, Digital and AI teams' policies, procedures, and programme implementation frameworks is desirable.</li> <li>Experience supporting the development of national AI strategies or assessments, digital transformation roadmaps, or innovation policies in the Arab States Region is an advantage.</li> <li>Familiarity with emerging global debates, standards, and policy frameworks related to AI trust and safety, digital ethics, and responsible AI implementation is desirable. Relevant work in the Arab Region is preferred.</li> <li>Experience working with Arab States Region government institutions, regulators, innovation hubs, academia, or private-sector technology partners is considered an asset.</li> </ul> Required Languages <ul> <li>Fluency in both written and spoken English and Arabic is required.</li> <li>Knowledge of another UN language, particularly French, is an asset.</li> </ul> Professional Certificates <ul> <li>N/A</li> </ul> Equal opportunity As an equal opportunity employer, UNDP values diversity as an expression of the multiplicity of nations and cultures where we operate and, as such, we encourage qualified applicants from all backgrounds to apply for roles in the organization. Our employment decisions are based on merit and suitability for the role, without discrimination.  UNDP is also committed to creating an inclusive workplace where all personnel are empowered to contribute to our mission, are valued, can thrive, and benefit from career opportunities that are open to all. Sexual harassment, exploitation, and abuse of authority UNDP does not tolerate harassment, sexual harassment, exploitation, discrimination and abuse of authority. All selected candidates, therefore, undergo relevant checks and are expected to adhere to the respective standards and principles.  Right to select multiple candidates UNDP reserves the right to select one or more candidates from this vacancy announcement.  We may also retain applications and consider candidates applying to this post for other similar positions with UNDP at the same grade level and with similar job description, experience and educational requirements. Use of AI by candidates Applicants are invited to read UNDP’s guidance for candidates on using AI responsibly in UNDP recruitment and selection Scam alert UNDP does not charge a fee at any stage of its recruitment process. For further information, please see www.undp.org/scam-alert. #LI-DNI </div>

Full Time 0 to 0 years Gulf Talnet

Director

On-site - Jordan - Amman 21 July 2026

Information Technology

<h4>Description</h4> A leading global pharmaceutical company is seeking a Director, Data & Analytics to take ownership of the strategy, architecture, delivery, and operational performance of its Data & 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. <h4>Responsibilities:</h4> 1. Platform architecture & engineering leadership: Own the enterprise data platform, Azure Data Lake Storage Gen2, Databricks lakehouse (Medallion architecture), Power BI, and Unity Catalog, ensuring it is architecturally sound, standardized, reliable, and engineered to scale. 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. Own the Unity Catalog governance model, RBAC, lineage, business glossary, and metric definitions, as the platform-enforced foundation for trusted data. 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. 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. Own data platform observability and operational excellence, pipeline reliability, SLA adherence, incident response, and data quality monitoring. 2. Delivery & data products: Run the D&A delivery programme, from source ingestion and pipeline engineering through to analytics, semantic layer, and data product delivery for business functions. Set and enforce delivery standards: sprint cadence, code review, documentation, testing, validation, and release management practices that govern all team output. Define and own data SLAs to the business, pipeline refresh frequency, availability, and incident response commitments, and ensure delivery is held against them. Own the data integration roadmap, prioritising ingestion of new source systems into the lake in alignment with business demand and platform readiness. 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. 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. 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. 3. Data governance & quality: Design and operate the data governance operating model, data ownership, stewardship, quality standards, business glossary, and metric definitions. Embed data quality gates into Medallion layer promotion, making quality a precondition of Bronze-to-Gold progression, with measurable thresholds and clear remediation paths. 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. Lead the technical evaluation, selection, and implementation of enterprise data catalogue tooling, including integration with Unity Catalog and the platform metadata layer. 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. Establish data quality measurement as a managed practice, KPIs, dashboards, periodic review, and accountability with data owners. 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. 4. Team leadership & capability development: Lead, hire, and develop the D&A team, Data Engineers, Analytics Engineers, BI Developers, and Business Analysts, sequencing hires in alignment with the platform roadmap and delivery demand. Define and evolve the role design, skills profile, and career framework for the team within the broader Data & AI CoE structure. Set the team's performance culture: clear ownership, high engineering standards, fast feedback, continuous learning, and documentation as a team discipline. Coach and develop team members directly, identifying high-potential individuals, investing in their technical and leadership growth, and building bench strength across roles. Manage team capacity and allocation across the platform roadmap and business delivery demand, ensuring focus stays on high-value work aligned to strategic priorities. Set the engineering craft culture, code review, design review, pairing, and shared technical standards, that lifts the technical quality of all team output. 5. Business partnership & vendor engagement: Serve as the senior D&A point of contact for business function leadership, translating business data needs into platform and delivery priorities. Build credibility with business stakeholders through reliable, consistent delivery, data products that are well-understood, well-documented, and match business expectations. Communicate proactively on platform status, delivery commitments, risks, and trade-offs, ensuring stakeholders have a current view and surfacing issues early. Represent D&A in cross-functional planning forums, ensuring the data foundation perspective is present in enterprise architecture, application, and AI investment decisions. Manage operational vendor and partner relationships for the data platform, Databricks, Microsoft Azure, Power BI, and implementation or augmentation partners. <h4>Requirements</h4> Bachelor's degree in Computer Science, Information Systems, Data Engineering, Mathematics, or related discipline. Master's degree in Data Science, Computer Science, Business Administration, or related field is preferred. Relevant certifications in cloud data platforms (e.g., Azure Data Engineer, Databricks Certified Data Engineer Professional) are preferred. Minimum 10 years of progressive experience in data engineering, data platform, or enterprise analytics roles. Minimum 5 years in a senior leadership role with direct team management accountability, hiring, developing, and performance managing a multi-disciplinary technical team. Proven hands-on production experience with Databricks (Delta Lake, Unity Catalog) and Azure data services at platform-design and engineering-lead level. Proven track record building or substantially remediating a cloud-native data platform in a complex, multi-source enterprise environment. Experience with Medallion/lakehouse architecture patterns in production. Experience designing data products and platform capabilities for AI/ML consumption, including Feature Store design, training dataset engineering, and ML data lineage is preferred. 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. Experience leading a data governance or catalogue implementation, from design through to business adoption. Life sciences, pharmaceutical, or other regulated industry experience is preferred. <h4>Skills:</h4> Technical competencies: Data platform architecture Data engineering, pipeline design & CDC patterns Data modelling, transformation & engineering standards Data governance, quality & catalogue Analytics & BI delivery MLOps & ML platform foundations Delivery management & agile methods Platform & technical skills: Deep practical expertise in Azure data services: ADLS Gen2, Azure Data Factory, Azure DevOps. Hands-on production experience with Databricks, Delta Lake, notebooks, Unity Catalog, MLflow. Strong understanding of incremental load and CDC patterns across enterprise source systems (SAP, Veeva, SuccessFactors, and similar). Power BI at the semantic layer level, understanding how the semantic layer should be designed for enterprise scale. CI/CD for data pipelines, practical implementation at production scale. Data quality frameworks, profiling, expectation testing, alerting, and remediation workflows. Working knowledge of modern data governance tooling: Unity Catalog, Collibra, Purview, or DataHub. Leadership & business skills: Credible with both technical teams and senior business stakeholders. Strong delivery discipline, owns commitments, communicates risks early, and sizes work realistically. Structured thinker, able to take a complex current state and produce a clear, prioritised, sequenced roadmap. Strong written and verbal communication in English; Arabic proficiency valued. Cultural intelligence for working effectively across MENA, US, and Europe.

Full Time 0 to 0 years Gulf Talnet

Director

On-site - Jordan - Amman 21 July 2026

Information Technology

<h4>Description</h4> A leading global pharmaceutical company is seeking a Director, Data & Analytics to take ownership of the strategy, architecture, delivery, and operational performance of its Data & 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. <h4>Responsibilities:</h4> 1. Platform architecture & engineering leadership: Own the enterprise data platform, Azure Data Lake Storage Gen2, Databricks lakehouse (Medallion architecture), Power BI, and Unity Catalog, ensuring it is architecturally sound, standardized, reliable, and engineered to scale. 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. Own the Unity Catalog governance model, RBAC, lineage, business glossary, and metric definitions, as the platform-enforced foundation for trusted data. 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. 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. Own data platform observability and operational excellence, pipeline reliability, SLA adherence, incident response, and data quality monitoring. 2. Delivery & data products: Run the D&A delivery programme, from source ingestion and pipeline engineering through to analytics, semantic layer, and data product delivery for business functions. Set and enforce delivery standards: sprint cadence, code review, documentation, testing, validation, and release management practices that govern all team output. Define and own data SLAs to the business, pipeline refresh frequency, availability, and incident response commitments, and ensure delivery is held against them. Own the data integration roadmap, prioritising ingestion of new source systems into the lake in alignment with business demand and platform readiness. 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. 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. 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. 3. Data governance & quality: Design and operate the data governance operating model, data ownership, stewardship, quality standards, business glossary, and metric definitions. Embed data quality gates into Medallion layer promotion, making quality a precondition of Bronze-to-Gold progression, with measurable thresholds and clear remediation paths. 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. Lead the technical evaluation, selection, and implementation of enterprise data catalogue tooling, including integration with Unity Catalog and the platform metadata layer. 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. Establish data quality measurement as a managed practice, KPIs, dashboards, periodic review, and accountability with data owners. 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. 4. Team leadership & capability development: Lead, hire, and develop the D&A team, Data Engineers, Analytics Engineers, BI Developers, and Business Analysts, sequencing hires in alignment with the platform roadmap and delivery demand. Define and evolve the role design, skills profile, and career framework for the team within the broader Data & AI CoE structure. Set the team's performance culture: clear ownership, high engineering standards, fast feedback, continuous learning, and documentation as a team discipline. Coach and develop team members directly, identifying high-potential individuals, investing in their technical and leadership growth, and building bench strength across roles. Manage team capacity and allocation across the platform roadmap and business delivery demand, ensuring focus stays on high-value work aligned to strategic priorities. Set the engineering craft culture, code review, design review, pairing, and shared technical standards, that lifts the technical quality of all team output. 5. Business partnership & vendor engagement: Serve as the senior D&A point of contact for business function leadership, translating business data needs into platform and delivery priorities. Build credibility with business stakeholders through reliable, consistent delivery, data products that are well-understood, well-documented, and match business expectations. Communicate proactively on platform status, delivery commitments, risks, and trade-offs, ensuring stakeholders have a current view and surfacing issues early. Represent D&A in cross-functional planning forums, ensuring the data foundation perspective is present in enterprise architecture, application, and AI investment decisions. Manage operational vendor and partner relationships for the data platform, Databricks, Microsoft Azure, Power BI, and implementation or augmentation partners. <h4>Requirements</h4> Bachelor's degree in Computer Science, Information Systems, Data Engineering, Mathematics, or related discipline. Master's degree in Data Science, Computer Science, Business Administration, or related field is preferred. Relevant certifications in cloud data platforms (e.g., Azure Data Engineer, Databricks Certified Data Engineer Professional) are preferred. Minimum 10 years of progressive experience in data engineering, data platform, or enterprise analytics roles. Minimum 5 years in a senior leadership role with direct team management accountability, hiring, developing, and performance managing a multi-disciplinary technical team. Proven hands-on production experience with Databricks (Delta Lake, Unity Catalog) and Azure data services at platform-design and engineering-lead level. Proven track record building or substantially remediating a cloud-native data platform in a complex, multi-source enterprise environment. Experience with Medallion/lakehouse architecture patterns in production. Experience designing data products and platform capabilities for AI/ML consumption, including Feature Store design, training dataset engineering, and ML data lineage is preferred. 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. Experience leading a data governance or catalogue implementation, from design through to business adoption. Life sciences, pharmaceutical, or other regulated industry experience is preferred. <h4>Skills:</h4> Technical competencies: Data platform architecture Data engineering, pipeline design & CDC patterns Data modelling, transformation & engineering standards Data governance, quality & catalogue Analytics & BI delivery MLOps & ML platform foundations Delivery management & agile methods Platform & technical skills: Deep practical expertise in Azure data services: ADLS Gen2, Azure Data Factory, Azure DevOps. Hands-on production experience with Databricks, Delta Lake, notebooks, Unity Catalog, MLflow. Strong understanding of incremental load and CDC patterns across enterprise source systems (SAP, Veeva, SuccessFactors, and similar). Power BI at the semantic layer level, understanding how the semantic layer should be designed for enterprise scale. CI/CD for data pipelines, practical implementation at production scale. Data quality frameworks, profiling, expectation testing, alerting, and remediation workflows. Working knowledge of modern data governance tooling: Unity Catalog, Collibra, Purview, or DataHub. Leadership & business skills: Credible with both technical teams and senior business stakeholders. Strong delivery discipline, owns commitments, communicates risks early, and sizes work realistically. Structured thinker, able to take a complex current state and produce a clear, prioritised, sequenced roadmap. Strong written and verbal communication in English; Arabic proficiency valued. Cultural intelligence for working effectively across MENA, US, and Europe.

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