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AI Synthetic Healthcare Record Generator

Savannah Watanabe

Contract · Job · New_York

About the role

We're building the next generation of privacy-preserving healthcare research tools, and hospital data partners keep asking for one thing we don't yet offer: statistically faithful synthetic patient records they can share freely with researchers. You will design generative models that produce synthetic electronic health records preserving clinically meaningful correlations, longitudinal patterns, and rare-condition distributions, while passing rigorous re-identification risk audits. This six-month engagement reports directly to our research lead and includes co-authorship opportunities on benchmark papers we plan to publish with two academic medical center partners.

Responsibilities

  • Design generative models producing synthetic EHR data with realistic longitudinal structure
  • Build and run re-identification risk audits against generated datasets
  • Benchmark synthetic data utility against real held-out clinical datasets
  • Collaborate with academic medical center partners on benchmark publications
  • Document generation assumptions and known limitations for downstream researchers

Requirements

  • 5+ years working with generative models or synthetic data techniques
  • Strong Python, PyTorch, and applied statistics background
  • Prior exposure to healthcare data structures (EHR, claims, or clinical trial data)
  • Comfortable working under HIPAA-adjacent data handling constraints

Benefits

  • Fully remote engagement
  • Publication and co-authorship opportunities
  • Flexible schedule across time zones
  • Extension likely based on outcomes

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Categories

data science & analytics AI & Machine Learning Engineers

Skills

python pytorch statistics

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Job Type

Contract

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

data science & analytics AI & Machine Learning Engineers

Skills

python pytorch statistics

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