وظائف محلل احصائي طبي في الأردن

<p><h4>Description</h4> <p>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.</p> <h4>Responsibilities:</h4> <ol> <li><strong>Strategy & architecture leadership</strong><br> 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.<br> 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.<br> Evaluate emerging AI technologies, frameworks, and platforms, providing technical direction and recommendations to senior IT and business stakeholders.<br> Lead architectural governance for all AI initiatives, ensuring solutions adhere to approved standards, security requirements, regulatory constraints, and scalability principles.<br> Define and maintain the enterprise AI technology stack, including cloud AI services (Azure, AWS, GCP), MLOps platforms, data platforms, and integration middleware.</li> <li><strong>AI solution design & technical oversight</strong><br> 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.<br> Produce high-quality architecture deliverables including solution design documents, architecture decision records (ADRs), technical specifications, data flow diagrams, and integration architecture blueprints.<br> Lead technical design reviews and architecture assessments for all AI initiatives in the portfolio, ensuring fitness for purpose, scalability, and compliance.<br> Define AI integration patterns with enterprise systems including SAP, MES, LIMS, CRM, and M365 ecosystems, ensuring seamless interoperability.<br> Guide AI developers in translating architecture designs into well-structured, maintainable, and production-ready solutions.<br> Oversee the design of MLOps pipelines including model training, validation, deployment, monitoring, and retraining workflows.</li> <li><strong>Platform engineering & infrastructure</strong><br> Own the architecture and governance of enterprise AI platforms including Microsoft Azure AI, Azure Machine Learning, Microsoft 365 Copilot, and other approved AI tooling.<br> Define cloud infrastructure architecture for AI workloads including compute, storage, networking, and security configurations aligned with the company IT standards.<br> Establish standards for model lifecycle management including versioning, registry, performance monitoring, drift detection, and retraining triggers.<br> 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.<br> Ensure AI platforms meet GxP validation, 21 CFR Part 11, and audit trail requirements where applicable.</li> <li><strong>Governance, compliance & responsible AI</strong><br> Embed regulatory and compliance requirements, including FDA AI/ML guidance, EMA requirements, GxP, GDPR, and HIPAA, into AI architecture design and review processes.<br> Define and enforce responsible AI architectural guardrails including model explainability, bias detection, fairness assessments, and human-in-the-loop design patterns.<br> Maintain AI architecture governance documentation including standards, patterns, approved toolsets, and deviation processes within the enterprise AI knowledge repository.<br> Coordinate with IT security, data privacy, legal, quality assurance, and regulatory affairs to ensure AI solutions meet all applicable oversight requirements.<br> Support E-AIAB governance processes by providing technical input into initiative assessments, vendor evaluations, and POV planning.</li> <li><strong>Technical leadership & enablement</strong><br> Provide technical mentorship, code and architecture reviews, and hands-on guidance to the AI developer team.<br> Define engineering best practices, coding standards, and DevOps/MLOps conventions for the AI team.<br> Collaborate with external vendors, implementation partners, and cloud providers to assess solutions, conduct technical due diligence, and ensure delivery quality.<br> Contribute technical expertise to vendor RFP/RFI processes, proof-of-concept evaluations, and contract assessments.<br> Represent the company's AI technical standards in cross-functional project delivery teams and steering committees.</li> <li><strong>Stakeholder engagement & communication</strong><br> Translate complex technical architecture concepts into clear, accessible language for business stakeholders, executive leadership, and non-technical audiences.<br> Serve as the primary technical escalation point for AI platform issues, architecture deviations, and integration challenges.<br> Collaborate with IT business partners and AI champions to provide technical feasibility input into AI opportunity assessments.<br> 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> <p>Bachelor's degree in computer science, information technology, software engineering, data science, or related technical field.<br> Master's degree in artificial intelligence, data science, computer science, or related discipline is preferred.<br> Microsoft Azure Solutions Architect Expert, Azure AI Engineer Associate, or equivalent cloud architecture certification is preferred.<br> TOGAF or equivalent enterprise architecture certification is preferred.<br> 7–10 years of professional experience in IT, software engineering, data & analytics, or AI/ML implementation.<br> 4–6 years of hands-on experience designing and delivering AI/ML solutions on cloud platforms (Azure, AWS, or GCP) in a production environment.<br> Proven track record of owning end-to-end AI solution architecture in a complex, cross-functional enterprise environment.<br> Experience with Microsoft Azure AI, Azure Machine Learning, and Microsoft 365 Copilot architecture and deployment is preferred.<br> Pharmaceutical, healthcare, life sciences, or other regulated industry experience is preferred.<br> Experience with GxP validation, 21 CFR Part 11, or regulatory technology compliance in an AI/ML context is preferred.</p> <h4>Skills:</h4> <p><strong>Technical competencies:</strong><br> 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)</p> <p><strong>AI & technology skills:</strong><br> Deep expertise in AI/ML architecture patterns, including supervised/unsupervised learning, NLP, computer vision, generative AI, and LLM-based solution design.<br> Strong hands-on proficiency with Azure AI services, Azure Machine Learning, MLflow, or equivalent MLOps tooling.<br> Solid experience designing and deploying generative AI solutions including RAG architectures, LLM orchestration (LangChain, Semantic Kernel), and enterprise copilot patterns.<br> Strong command of enterprise integration architecture including REST APIs, event-driven architecture, message queues, and middleware platforms.<br> Proficiency in cloud infrastructure design including IaC (Terraform, Bicep), containerization (Docker, Kubernetes), and CI/CD pipelines.<br> Strong understanding of data architecture components including data lakes, lakehouses, feature stores, and vector databases.<br> Working knowledge of pharmaceutical business processes including GxP operations, quality management systems, and regulatory affairs workflows (preferred).<br> Solid understanding of AI governance frameworks, responsible AI principles, data privacy regulations (GDPR, HIPAA), and IT security principles relevant to AI deployment.</p></p><p></p>

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

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