At AstraZeneca, we are committed to advancing science and improving patient outcomes through innovation, collaboration, and data-driven decision-making. As healthcare becomes increasingly connected, the ability to translate complex data into meaningful insights is more important than ever. We are looking for a Data Health Population Specialist to help shape local data strategy, deliver impactful analytics and dashboard solutions, and support smarter decisions across the healthcare ecosystem.

This role is accountable for the end-to-end delivery of data, analytics, and dashboard solutions, from data source identification and collection through preparation, analysis, visualisation, deployment, and ongoing maintenance. Working closely with cross-functional partners, you will help ensure data assets are reliable, compliant, and transformed into clear analytical products that support business and medical priorities.

What you’ll do

As a Data Health Population Specialist, you will define and maintain local data strategy aligned to business and medical needs, with a strong focus on reporting, analytics, and dashboard delivery. You will identify and assess internal and external data sources, support data governance and compliance requirements, and help ensure data quality, accessibility, and consistency across analytical workflows.

You will coordinate data collection, integration, cleaning, transformation, and harmonisation from multiple sources, helping to build analysis-ready datasets for reporting and visualisation. You will also contribute to the development of documentation, data definitions, metadata, refresh processes, and business rules that support sustainable and trusted data use.

A key part of the role will be leading the end-to-end development of dashboards, from business requirement gathering through KPI design, visualisation, testing, publication, and continuous improvement. You will work closely with stakeholders to ensure dashboards are intuitive, actionable, and aligned with business questions and decision-making needs.

You will also perform descriptive and exploratory analytics to identify trends, patterns, gaps, and opportunities across healthcare and business data. This includes supporting analytical use cases such as cohort building, treatment patterns, incidence and prevalence analysis, and population health reporting. Your ability to translate findings into clear insights and compelling visual outputs will be essential.

In addition, the role will support the practical and compliant use of AI-enabled tools to improve efficiency in areas such as data preparation, ETL validation, documentation, testing, script review, and reporting workflows. You will work with internal teams to ensure AI-supported processes are responsibly governed, documented, and subject to appropriate human oversight.

Essential requirements

We are looking for candidates with a Master’s degree in Health/Medical Informatics, Mathematics, Statistics, Computer Science, Life Sciences, or a related quantitative field, together with 4+ years of experience in data analytics, dashboard development, business intelligence, reporting, or data management.

You should bring strong technical capability in SQL, SAS, R, and/or Python, along with hands-on experience in Power BI, Tableau, or similar dashboarding tools. Experience in end-to-end dashboard development is essential, including data sourcing, transformation, KPI definition, visualisation, testing, deployment, and maintenance.

You will also have strong experience in data preparation, integration, validation, and ETL/reporting logic checks, together with a sound understanding of healthcare data structures, coding systems, KPI definitions, and the limitations of secondary healthcare data.

A good understanding of GDPR, local health data regulations, de-identification, and data governance practices is required. You should also be comfortable translating business or medical needs into practical data strategy, analytics plans, and dashboard solutions, while working effectively across cross-functional and matrix environments.

Experience or familiarity with AI-enabled tools for process improvement, validation, documentation, or analytics acceleration is also important, provided they are used in a compliant and controlled environment.

Desirable requirements

Experience with OMOP CDM, OHDSI tools, and ATLAS would be advantageous, as would experience with synthetic data, test data design, or privacy-conscious validation approaches.

We would also welcome candidates with exposure to MLOps, cloud platforms such as Azure, and the use of AI-assisted tools for coding, automation, or documentation. Experience building healthcare dashboards, defining reporting strategy, and supporting analytics storytelling would be valuable, as would familiarity with privacy-enhancing techniques and fairness assessments in regulated settings.

Why AstraZeneca?

At AstraZeneca, you’ll be part of a business where science, technology, and collaboration come together to improve patient outcomes. This role offers the opportunity to work at the intersection of data strategy, health analytics, dashboard development, and AI-enabled process improvement, helping to build solutions that support real-world decisions and practice change.

If you are motivated by turning complex data into actionable insights and want to contribute to a high-impact, cross-functional environment, this is an opportunity to make a meaningful difference.

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.