Job Title: Observational Health Data Analyst

Location: Remote/Hybrid (no preference to remote or in person)

Department: Global Epidemiology

Contract: 1 year Contract with possiblity of extension

About the Role

The Client's Global Epidemiology group is collaborating with a leading external data network on a major initiative focused on Lupus. We are seeking an experienced Observational Health Data Analyst to lead the analysis of diverse observational healthcare datasets. This is a high-impact role that requires a sharp analytical mind, deep understanding of real-world health data, and the technical skills to deliver high-quality insights that drive scientific and strategic decision-making.

The ideal candidate is a self-starter, team player, and problem solver with a passion for working with complex, real-world data to answer meaningful health questions.

Key Responsibilities

• Lead and manage the analysis of observational health data across a federated data network.
• Perform data characterization, data quality assessments, and recommend improvements for data quality.
• Develop and apply statistical methodologies and database programming techniques using R and SQL.
• Collaborate with European registry sites and data owners, crafting and sending detailed queries to better understand and interpret the data.
• Evaluate incoming site-level results for consistency and data quality; provide written recommendations for data cleaning or refinement.
• Use observational data to answer key research questions related to the safety, effectiveness, and potential use of drug products in the Lupus therapeutic area.
• Write analytic code and build visualizations using the OHDSI tool stack and relevant R packages.
• Contribute to internal documentation, reporting, and presentations for cross-functional stakeholders.

Day-to-Day Activities

• Partnering with data owners to review data and ensure understanding across multiple data sources (mostly registry data).
• Running analyses on already-standardized observational data (converted to OMOP/Common Data Model formats).
• Translating scientific or business questions into structured data queries and actionable insights.
• Engaging in regular collaboration with epidemiologists, clinicians, and data partners across Europe.
• Managing analysis timelines, priorities, and documentation to ensure reproducibility and transparency.

Required Qualifications

• 3–5 years of hands-on experience analyzing observational health data or working with real-world data (RWD) in healthcare.
• Strong proficiency in R and SQL for data analysis and statistical modeling.
• Demonstrated experience working with registry data and federated data networks.
• Familiarity with Observational Outcomes Partnership (OOP) data models or similar standard data models (e.g., OMOP CDM).
• Experience conducting data quality assessments, exploratory data analysis, and generating insights from complex data sets.
• Excellent communication skills, especially in working with external collaborators and non-technical stakeholders.

Preferred Qualifications

• Hands-on experience with OHDSI tools and R packages (e.g., Atlas, Achilles, FeatureExtraction, CohortMethod).
• Prior exposure to OMOP Common Data Model and associated analysis workflows.
• Background in epidemiology, biostatistics, health informatics, or a related quantitative health field.
• Experience working with messy, imperfect healthcare data – strong intuition around data cleaning, validation, and usability.