Senior Principal Scientist/Principal Scientist, Real-World Evidence & Disease Genomics

About The Job

Join Sanofi's Target, Disease and Systems Biology (TDSB) team and become a key contributor to our Disease Genetics and Genomics cluster. In this role, you will sit at the intersection of real-world evidence generation and disease genomics, leveraging large-scale electronic health records (EHRs) and population biobanks to transform how we understand disease trajectories and evaluate indication expansion opportunities. Working alongside world-class computational and experimental scientists, you will help shape the future of drug discovery and development across Sanofi's therapeutic areas — from early indication prioritization to business development evaluation of external assets.

Main Responsibilities

• Lead indication prioritization and expansion for Sanofi R&D assets and external assets by generating target trial emulation from large-scale EHR datasets and biobanks, providing strategic guidance to therapeutic program teams
• Develop and implement methods for the analysis of longitudinal medical records, including classical statistical models and EHR foundation models, to evaluate the impact of drugs on disease trajectories through target trial emulation frameworks
• Define and champion well-curated disease cohorts and disease progression endpoints using EHRs, working in close coordination with disease area and medical expert teams
• Run genome-wide association studies (GWAS) in curated patient cohorts to identify genetic drivers of disease and therapeutic response
• Apply and scale these methods across large population biobanks, including FinnGen and All of Us, to generate robust, reproducible evidence supporting Sanofi's R&D pipeline

About You

• Basic Qualifications: Ph.D. in Biomedical Informatics, Epidemiology, Computational Biology, Human Genetics, or a related field, with 4+ years of post-PhD experience, preferably in the biopharma or biotech industry
• Excellent knowledge of and hands-on experience working with longitudinal EHR datasets and OMOP common data models
• Knowledge of target trial emulation methods and causal inference frameworks applied to observational data
• Familiarity with GWAS methodology and population-scale genomic analyses
• Good knowledge of diseases and medical practices, with the ability to engage meaningfully with clinical and disease area experts
• Strong coding skills and demonstrated experience leveraging AI-assisted coding tools such as Cursor or Claude Code to accelerate scientific workflows
• Excellent communication skills, with the ability to present complex analytical findings clearly to both scientific and non-scientific stakeholders, and to influence strategic decisions
• Genuine enthusiasm for working in an agile, fast-paced team environment where priorities evolve rapidly
• Impact-oriented mindset with a strong drive to see scientific work translate into real R&D decisions
• Experience working with large biobank platforms such as FinnGen, UK Biobank, or All of Us
• Experience working in Trusted Research Environments (TREs) or cloud computing platforms (AWS, GCP)
• Experience collaborating across cross-functional teams including clinical development, translational medicine, and business development
• Experience with agentic AI frameworks and their application to biomedical data analysis or workflow automation