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AI Patient Stratification Model Developer

James Yamamoto

Full-time · Job · New_York

About the role

Clinical trials fail at 90% because the wrong patients are enrolled. Too much heterogeneity in the patient population masks treatment effects; too narrow enrollment criteria slow recruitment to a crawl and produce results that don't generalize. Patient stratification — identifying which patients are most likely to respond to a specific treatment — is one of the highest-value applications of ML in drug development. Our platform helps pharma companies design better trials by predicting which patient subgroups will respond, identifying biomarker signatures associated with responders, and simulating trial outcomes under different enrollment criteria. The models run on electronic health record data, genomic profiles, and historical trial results. You'll develop the stratification models for two new therapeutic areas currently in our pipeline — oncology and autoimmune disease. Each has different data modalities, different biomarker hypotheses, and different regulatory sensitivities.

Responsibilities

  • Develop patient stratification models for oncology and autoimmune therapeutic areas
  • Build biomarker signature discovery pipelines from multi-modal clinical data
  • Implement trial simulation models evaluating enrollment criteria against outcome projections
  • Validate model outputs against external trial datasets and published literature
  • Collaborate with medical scientists to incorporate biological hypotheses into model design
  • Document model methodology in formats suitable for regulatory submission support

Requirements

  • 3+ years in ML engineering with healthcare, clinical, or life sciences data experience
  • Experience with survival analysis, patient cohort modeling, or clinical trial statistics
  • Strong Python and statistical computing skills
  • Familiarity with EHR data structures or clinical data standards like OMOP
  • Ability to work with small, high-quality labeled datasets typical of clinical research

Benefits

  • Fully remote
  • Health and dental
  • $3,000 annual professional development budget
  • 401(k) with match
  • Equity participation

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Categories

data science & analytics AI & Machine Learning Engineers

Skills

python scikit-learn pandas statistics sql

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Job Type

Full-time

Level

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Language

English

Salary Range

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AI Expertise

data science & analytics AI & Machine Learning Engineers

Skills

python scikit-learn pandas statistics sql pytorch

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