Senior Principal Scientist, Real-World Evidence & Disease Genomics

Sanofi

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Cambridge, MA
Salary
$148,500–$214,500 / yr
Posted
72 days ago
Freshness
Confirmed live yesterday
Closes
Sep 27, 2026

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $197k
This role $182k
$129k most similar roles pay here $246k

This role pays more than 50% of similar roles. Most pay $162,000–$231,812 — the shaded band above. At the midpoint, this role pays about $182k versus about $197k for comparable roles.

Based on 240 similar postings.

Employer

About Sanofi

Sanofi is a French global R&D-driven and AI-powered biopharmaceutical company focused on human health through specialized medicine, vaccines, and consumer healthcare.

Sanofi currently has 4 open roles on FindRole.

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At a glance

TL;DR · Senior Principal Scientist, Real-World Evidence & Disease Genomics

Senior Principal Scientist/Principal Scientist, Real-World Evidence & Disease Genomics joins the Target, Disease and Systems Biology team within the Disease Genetics and Genomics cluster. This role sits at the intersection of real-world evidence generation and disease genomics to transform how the organization understands disease trajectories and evaluates indication expansion opportunities. The successful candidate will develop EHR-based methods, including foundation models and classical statistical approaches, to generate evidence at scale. Key responsibilities include defining curated disease cohorts, conducting genome-wide association studies (GWAS) in patient cohorts, and managing engagement with population biobanks like FinnGen and All of Us. Required expertise includes longitudinal EHR datasets, OMOP common data models, target trial emulation, causal inference frameworks, and AI-assisted coding tools. The role addresses the technical challenge of translating large-scale electronic health records into actionable R&D strategies for drug discovery and development.

What you'll do

  • Develop and implement methods using EHR foundation models and statistical approaches to analyze longitudinal medical records.
  • Generate real-world evidence from large-scale electronic health records to evaluate drug impacts on disease trajectories.
  • Define and curate disease cohorts and progression endpoints to support target trial emulation and genomic analyses.
  • Conduct genome-wide association studies (GWAS) in curated patient cohorts to identify genetic drivers of disease and treatment response.
  • Manage engagement with population biobanks like FinnGen and All of Us to generate insights for R&D strategy.
  • Lead indication prioritization and expansion for internal and external assets using target trial emulation frameworks.
  • Build scalable, reproducible analytical pipelines for large-scale data processing in trusted research environments.

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.
  • Hands-on experience with longitudinal EHR datasets and OMOP common data models.
  • Knowledge of target trial emulation methods and causal inference frameworks for observational data.
  • Familiarity with GWAS methodology and population-scale genomic analyses.
  • Strong coding skills including experience with AI-assisted tools like Cursor or Claude Code.
  • Experience working with large biobank platforms such as FinnGen, UK Biobank, or All of Us.
  • Ability to develop scalable analytical pipelines in Trusted Research Environments (TREs) or cloud platforms like AWS and GCP.

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