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AI Adverse Drug Reaction Signal Detection Engineer

James Yamamoto

Full-time · Job · New_York

About the role

Pharmacovigilance is legally required but functionally broken at most pharmaceutical companies. FDA receives over 2 million adverse event reports per year; less than 10% are identified as genuinely novel safety signals. The rest are noise, duplicates, or expected events. The teams responsible for reviewing these reports are overwhelmed, and the signals that matter most — rare events that only emerge at population scale, or unexpected drug-drug interactions — get missed. We build signal detection systems for three of the top-10 pharma companies. The core problem is statistical: detecting a safety signal in a sea of expected adverse events requires disproportionality analysis, case series clustering, and temporal pattern detection that standard pharmacoepidemiology methods handle poorly when the signal is subtle or delayed. You'll design and implement the ML layer of our signal detection pipeline: building models that combine spontaneous report data with electronic health records, clinical trial data, and literature signals to produce risk-stratified alerts that pharmacovigilance teams can act on. You'll work closely with clinical pharmacologists who understand the biology and regulatory scientists who understand what signals require action.

Responsibilities

  • Build disproportionality analysis and ML-based signal detection for adverse event databases
  • Integrate spontaneous report data with EHR, clinical trial, and literature signals
  • Design risk stratification models that prioritize alerts by clinical significance
  • Develop temporal pattern detection for delayed and cumulative adverse effects
  • Build evaluation frameworks validated against confirmed historical safety signals
  • Collaborate with clinical pharmacologists and regulatory scientists on signal criteria

Requirements

  • 6+ years in ML or biostatistics with pharmacovigilance or drug safety domain experience
  • Experience with FAERS, VigiBase, or similar spontaneous reporting databases
  • Strong Python, statistical modeling, and data pipeline skills
  • Understanding of disproportionality analysis methods: PRR, ROR, Bayesian approaches
  • Ability to collaborate with medical and regulatory domain experts

Benefits

  • Fully remote with flexible schedule
  • Top-of-market compensation
  • Comprehensive health benefits
  • $5,000 annual professional development budget
  • Equity participation

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn pandas spark

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Language

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Salary Range

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AI Expertise

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Skills

python pytorch scikit-learn pandas spark statistics

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