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AI Drug Efficacy Stratification Researcher

James Yamamoto

Full-time · Job · Boston

About the role

A drug that shows modest average efficacy in a phase III trial may be highly effective for a specific patient subpopulation and ineffective for others. Biomarker-stratified efficacy analysis — identifying which patient characteristics predict strong response — is how personalized medicine translates from concept to clinical practice. It's also where FDA approval for companion diagnostic labeling is won or lost. We partner with 14 pharmaceutical companies on late-stage clinical development. Efficacy stratification modeling is among the highest-stakes analyses we perform: the subgroups we identify influence labeling decisions, affect which patients can access the drug, and determine post-approval companion diagnostic requirements. The analysis must be statistically rigorous, reproducible, and defensible to FDA statistical reviewers. You'll lead efficacy stratification modeling for our partner programs: designing biomarker discovery analyses on trial datasets, implementing subgroup identification approaches that control for multiple testing and data dredging, and producing the statistical analysis plans and results packages submitted to regulatory agencies. Publications in clinical pharmacology or personalized medicine venues are expected. The role is hybrid — 3 days per week in our Cambridge, MA office.

Responsibilities

  • Design biomarker-stratified efficacy analyses for late-stage clinical trial datasets
  • Implement subgroup identification methods with rigorous multiple testing correction
  • Produce regulatory-grade statistical analysis plans and results packages for FDA submission
  • Collaborate with clinical pharmacology and biostatistics teams on analysis design
  • Develop reproducible analysis pipelines satisfying FDA data integrity requirements
  • Publish findings in clinical pharmacology and personalized medicine venues

Requirements

  • 8+ years in clinical data science, biostatistics, or translational ML for drug development
  • Deep understanding of clinical trial design, biomarker analysis, and regulatory statistics
  • Strong Python and statistical computing skills with survival analysis and mixed-effects modeling experience
  • Experience producing regulatory-grade statistical documentation for FDA interactions
  • Publication record in clinical pharmacology, oncology, or personalized medicine

Benefits

  • Hybrid: 3 days Cambridge MA, 2 days remote
  • Top-of-market compensation reflecting regulatory expertise requirements
  • Research conference and publication budget
  • Comprehensive health benefits
  • Significant equity participation

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn statistics spark

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Level

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Language

English

Salary Range

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AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn statistics spark pandas

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