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Applied AI Scientist

Wyatt Tanaka

Full-time · Job · Chicago

About the role

Our product is a clinical decision support system used by 40 hospitals across the Midwest. The models behind it predict patient deterioration, flag drug interactions, and recommend treatment adjustments — and they need to be right. In healthcare AI, false negatives have real consequences. As an applied AI scientist, you'll own the modeling pipeline for our patient risk scoring system. That means everything from cohort selection and feature engineering on EHR data to model validation and bias auditing. You'll work closely with our clinical advisory board (practicing physicians) to ensure the models are clinically meaningful, not just statistically accurate. We need someone who respects the domain complexity and can navigate the tension between model performance and clinical interpretability. Published research is a plus, but what we really value is judgment.

Responsibilities

  • Develop and validate predictive models for patient risk scoring
  • Conduct bias audits and fairness evaluations on clinical models
  • Collaborate with clinical advisors to ensure model interpretability
  • Design feature engineering pipelines on electronic health record data
  • Present model performance reports to hospital partners

Requirements

  • 5+ years in applied ML or data science
  • Experience with healthcare data (EHR, claims, or clinical trial data)
  • Strong statistical foundation including causal inference
  • Python, PyTorch, and scikit-learn proficiency
  • Familiarity with model interpretability techniques (SHAP, LIME)

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn statistics sql

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn statistics sql pandas

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