Data Health Population Specialist

Job Title: Data Population Health Specialist

Location: Lisbon, Portugal

Introduction to the Role:

The Data Population Health Specialist is accountable for end‑to‑end stewardship of real‑world data (RWD) sources—spanning acquisition, governance, integration, quality management, and analytical enablement—to support evidence generation and local practice change. The role translates medical and business needs into pragmatic data and analytics plans, enabling compliant, reliable, and scalable delivery of real‑world evidence (RWE), including advanced analytics and machine learning (ML) use cases.

About the Role:

This position reports to the Head of Evidence Generation & Data Impact and works within the Evidence Generation & Data Impact team. Working closely with the Evidence Generation & Data Impact team, the Specialist partners cross‑functionally with Medical Affairs, Marketing, Market Access, Procurement, R&D Information/IT, and Digital/IBEX. Together, these teams generate medical evidence and demonstrate the value of AstraZeneca medicines for patients, healthcare professionals, payers, and other decision‑makers.

The Evidence Generation & Data Impact team plans and executes local projects across all products to build confidence in AZ therapies. The team implements observational research—including analyses using RWD—and oversees externally sponsored research defined by the Local Cross‑Functional Evidence Team.

Accountabilities

This position sets the local real‑world data and analytics strategy and maintains a visible quarterly roadmap aligned to medical objectives. It establishes and enforces data governance—policies, metadata, lineage, and role‑based access—ensuring compliance and audit readiness. Secure data provisioning processes are defined, and the data catalog is maintained so datasets are discoverable with clear lineage, quality, and usage constraints. Local RWD across claims, EHR/EMR, registries, labs, and pharmacies is sourced and evaluated; licenses and SLAs are managed so deliveries arrive on schedule and meet fitness‑for‑use. ETL/ELT pipelines are built and operated to reliably ingest and harmonize data to OMOP and core coding systems, with quality monitored against agreed thresholds for completeness, consistency, conformance, and timeliness. De‑identification and linkage safeguards preserve privacy while enabling longitudinal analysis.

Analysis‑ready datasets and feature stores are curated to support reproducible studies and machine learning. The model lifecycle—validation, monitoring, and retraining—is coordinated to sustain calibration, fairness, and performance. Study protocols are translated into executable cohorts and pipelines with traceability from raw data to final results. Population health metrics and dashboards are delivered for use in clinical and payer decisions. Documentation standards for cohorts, transformations, and methods are implemented, and controls are updated in step with regulatory changes across GDPR and local requirements. Work is planned and tracked in a matrixed environment with clear ownership and risk management. Incident and issue processes address defects, outages, and model drift with root‑cause actions. Environment and tooling readiness (compute, storage, security, ATLAS/MLOps integrations) is ensured, security reviews are coordinated with IT to meet enterprise standards, and adoption and impact are tracked so analytics demonstrate measurable value and inform continuous improvement.

Essential Skills and Experience

• Data strategy, governance, and compliance experience, including policy and standard setting, metadata and lineage, and privacy/regulatory adherence.
• RWD source evaluation and vendor management with licensing, SLAs, and onboarding.
• Data engineering leadership across ETL/ELT, common data models (e.g., OMOP), coding system mappings, and data quality management.
• Analytical enablement with statistical and ML methods, feature stores, model lifecycle management, and MLOps practices.
• Population health analytics and RWE delivery with protocol translation, cohort construction, and traceable pipelines.
• Cross‑functional stakeholder engagement and delivery management within a matrix organization.

Desirable Skills and Experience

• Advanced degree in epidemiology, biostatistics, computer science, data science, or related field.
• Hands‑on proficiency in Python and SQL and experience with cloud platforms and analytics (e.g., Azure, Databricks, Spark).
• Experience with OMOP/OHDSI tools (e.g., ATLAS) and health coding systems (ICD‑9/ICD‑10, SNOMED CT, LOINC, ATC).
• Applied experience in causal inference, interpretability techniques (e.g., SHAP), and MLOps in healthcare.
• Experience delivering end‑to‑end RWE studies in pharmaceutical, biotech, payer, or provider settings.
• Vendor management and contracting, including SLAs and data quality oversight.
• Publications or conference presentations in RWE, epidemiology, or applied ML in healthcare.
• Fluency in English; additional local language proficiency is an advantage.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That’s why we work, on average, a minimum of three days per week from the office. But that doesn't mean we’re not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

About AstraZeneca

AstraZeneca is a global, science‑led company that discovers, develops, and delivers medicines. Our teams use modern data and technology to generate evidence that informs studies, launches, and care pathways. We value accountability, clear feedback, and collaborative problem‑solving, and we invest in tools and practices that support scalable, reliable solutions in health and sustainability. We are an equal opportunity employer and are committed to an inclusive workplace. If you need a reasonable accommodation at any stage of the recruitment process (application, interview, or onboarding), please let us know. We will provide adjustments to help you participate fully and equitably.

If you are ready to lead the end-to-end real-world data agenda and turn population insights into life-changing impact, submit your application and make your mark today!

Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.