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AI Clinical Outcome Prediction Researcher

Alice Rossi

Full-time · Job · New_York

About the role

We serve 28 health systems and academic medical centers. Our NLP systems already extract structured information from clinical notes. The next capability our clients want — and are willing to pay significantly for — is predictive: given a patient's clinical record at time of admission, what are their likely outcomes? ICU admission probability. 30-day readmission risk. Length of stay distribution. Complications conditional on proposed treatment path. Clinical outcome prediction is harder than most ML applications because the training data reflects clinical decisions, not random assignment. Patients who receive intensive early intervention are systematically different from those who don't, and naive models learn those correlations without disentangling intervention effects from risk factors. You need causal reasoning, not just pattern matching. You'll lead the outcome prediction research program: designing models that account for treatment selection bias, validating against held-out hospital systems, and implementing the fairness testing that ensures models perform equitably across patient demographics. You'll publish your methods — our partners expect research-grade rigor, not black-box systems. The role is hybrid: 2 days per week in our Boston office for clinical collaborator meetings.

Responsibilities

  • Design clinical outcome prediction models accounting for treatment selection bias
  • Implement causal inference techniques to separate risk factors from intervention effects
  • Validate models across held-out hospital systems with different patient populations
  • Develop fairness testing ensuring equitable performance across demographic groups
  • Publish methodology in clinical informatics or ML-for-health venues
  • Present findings to clinical partners and hospital leadership

Requirements

  • 7+ years in ML research with clinical or health data science experience
  • Deep expertise in causal inference and confounding adjustment in observational data
  • Strong Python and statistical computing skills with large EHR dataset experience
  • Publication record in clinical ML, bioinformatics, or related health informatics venues
  • Ability to collaborate with physicians and clinical informaticists

Benefits

  • Hybrid: 2 days in Boston office, 3 days remote
  • Top-of-market compensation for clinical ML expertise
  • Research conference and publication budget
  • Comprehensive health benefits
  • Equity participation

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn spark statistics

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$200k+ / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn spark statistics pandas

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