Senior Principal Scientist, Real-World Evidence & Disease Genomics

What you'll do

• Lead indication prioritization and expansion for R&D and external assets using large-scale EHR datasets.
• Develop and implement methods for analyzing longitudinal medical records using classical statistical models and foundation models.
• Define and champion curated disease cohorts and progression endpoints in coordination with medical experts.
• Run genome-wide association studies (GWAS) in curated patient cohorts to identify genetic drivers of disease.
• Apply analytical methods to population biobanks like FinnGen and All of Us to support R&D strategy.
• Generate target trial emulations from electronic health records to evaluate drug impacts on disease trajectories.
• Develop scalable and reproducible analytical pipelines for large-scale data processing.

What we're looking for

• Ph.D. in Biomedical Informatics, Epidemiology, Computational Biology, Human Genetics, or a related field.
• 4+ years of post-PhD experience, preferably in the biopharma or biotech industry (preferred).
• 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.
• Strong coding skills and experience leveraging AI-assisted coding tools such as Cursor or Claude Code.
• 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 like AWS or GCP.