Senior Data Scientist

You will design, execute, and deliver rigorous, decision-relevant real-world evidence analyses while helping to advance a scalable, AI-enabled analytics capability.

This is a hands-on role that combines epidemiological expertise, advanced analytics, and data science to deliver high-quality evidence and actionable insights.

The successful candidate will bring deep expertise in epidemiology, analytical programming, observational research, statistical methods, and real-world data.

Working closely with the Head of Rapid Analytics and stakeholders across the EpiRWE therapy areas, including Virology, Oncology, and Inflammation, you will translate evidence needs into fit-for-purpose study designs, analytical requirements, executable plans, and high-quality results.

Key Responsibilities

EpiRWE Analytics Delivery

• Partner with stakeholders to define evidence needs and translate them into clear research questions and analytical plans using OMOP and native datasets.
• Design, execute, interpret, and report observational and epidemiologic studies supporting assigned assets, indications, and therapeutic areas.
• Conduct feasibility analyses, methodology development, statistical analysis planning, cohort definition, programming, quality control, results interpretation, and final reporting.
• Ensure that analytical approaches are scientifically rigorous, fit for purpose, and aligned with the clinical, medical, regulatory, safety, or access decision being supported.
• Provide guidance on study design, data-source selection, epidemiologic methods, potential biases, confounding, sensitivity analyses, and interpretation of findings.

Study Design and Analytical Methods

• Apply advanced observational research methods to address complex research questions.
• Evaluate assumptions, limitations, data completeness, measurement validity, confounding, selection bias, and generalizability.
• Develop and review table shells, analytic data specifications, statistical analysis plans, derived variables, statistical models, and quality-control strategies.
• Perform and oversee analyses using R, SQL, and Python.
• Apply version control and reproducible research practices to analytical code, cohort definitions, specifications, documentation, and study outputs.

OMOP CDM, OHDSI, and Federated Analytics

• Support the scientific and analytical use of the OMOP Common Data Model and the OHDSI ecosystem for rapid, standardized, and reproducible real-world evidence generation.
• Translate study requirements into standardized concepts, concept sets, cohort definitions, temporal logic, inclusion and exclusion criteria, and analytical specifications.
• Use and adapt OHDSI open-source tools and methods to support cohort development, cohort diagnostics, characterization, feasibility assessment, and multi-database analytics.
• Develop reusable cohorts, phenotypes, concept sets, analytical modules, and study packages that can be applied across data sources, therapeutic areas, and research questions.
• Support federated analytics by developing portable and well-documented study specifications that can be executed by approved data partners without transferring patient-level data.
• Evaluate cross-database differences in coding, data capture, observation time, population characteristics, and analytical results.

Data-Source Evaluation and Feasibility

• Conduct feasibility assessments to match research objectives with the most appropriate real-world data sources.
• Extend analytical approaches beyond OMOP-based data when registries or other non-OMOP sources are better suited to the research question.

Rapid Analytics and Automation

• Identify opportunities to improve EpiRWE delivery through automation, reusable workflows, structured analytical specifications, and AI-enabled tools.
• Define how reusable tools, agents, and human review checkpoints can support efficient and scalable analytical workflows.
• Translate epidemiology and RWE needs into technical requirements, user stories, acceptance criteria, solution designs, and delivery priorities for engineering partners.
• Partner with AI, data, and software engineers to develop solutions supporting concept-set development, cohort construction, feasibility analyses, cohort diagnostics, quality control, documentation, and analytical reporting.
• Define validation criteria and quality standards for automated workflows and AI-enabled solutions.
• Review and validate AI-generated or automated outputs for scientific accuracy, completeness, consistency, traceability, and alignment with study protocols.
• Ensure that automation enhances analytical quality, efficiency, and scalability while maintaining appropriate scientific oversight.

Capability Building and Stakeholder Impact

• Help establish EpiRWE analytics as a scalable organizational capability rather than a collection of one-off studies.
• Develop reusable templates, analytical standards, cohort libraries, study packages, quality-control frameworks, and documentation practices.
• Measure capability value through improvements in delivery speed, analytical quality, reproducibility, reuse, stakeholder adoption, and decision impact.
• Promote a culture of continuous improvement that challenges inefficient processes while maintaining scientific rigor and appropriate governance.
• Contribute to the development of analytical best practices, reusable assets, and evidence-generation approaches across EpiRWE and cross-functional teams.

Required Qualifications

• Advanced degree in Epidemiology, Biostatistics, Health Services Research, Outcomes Research, Public Health, Biomedical Informatics, Data Science, or a related quantitative field.
• Demonstrated expertise in epidemiologic study design, statistical analysis, causal inference, and the evaluation of bias and confounding.
• Experience working with healthcare claims, EHR, registry, laboratory, linked, or other real-world data sources.
• Hands-on analytical programming experience using R, SQL, and Python.
• Experience developing and applying machine learning, AI, or advanced analytical methods to real-world data and evidence-generation use cases.
• Experience with version control and reproducible analytical workflows.
• Ability to translate scientific questions into protocols, analysis plans, analytical specifications, technical requirements, and delivery plans.
• Strong understanding of data quality, analytical validation, quality control, traceability, and documentation.
• Ability to communicate complex analytical findings clearly to both technical and non-technical stakeholders.

Why Join? Those who join us become part of a recognized global leader still willing to challenge the status quo to improve patient care.

Benefits No specific benefits mentioned.