Applied Data Scientist, Health AI Evaluation & Datasets

5+ years of data science experience, including at least 2+ years with healthcare, clinical, biomedical, payer, provider, pharma, life sciences, or comparable regulated health data.

Working knowledge of healthcare data and standards: EHR structure, clinical documentation conventions, ICD-10, CPT, SNOMED CT, LOINC, RxNorm, and at least passing familiarity with FHIR, HL7, or equivalent interoperability concepts.

Hands-on experience designing ML datasets, not just consuming them: writing annotation guidelines, sizing cohorts, setting quality thresholds, designing QA checks, and shipping data that downstream teams can train or evaluate on.

Familiarity with LLM-based health AI workflows, including prompt design, rubric-based evaluation, retrieval-augmented generation, LLM-as-judge methods, model comparison, and the limitations of automated evaluation in clinical contexts.

Strong Python and SQL; comfort with pandas, scikit-learn, statsmodels or equivalent tools; and working familiarity with modern LLM tooling such as Hugging Face, evaluation frameworks, prompt development tools, or model APIs.

Statistical literacy across sampling design, bias and fairness analysis, inter-annotator agreement metrics (Cohen or Fleiss kappa, Krippendorff alpha), confidence intervals, significance testing where appropriate, error analysis, and the ability to push back when a number is being over-interpreted.

Solid grasp of healthcare privacy, compliance, and governance: HIPAA, de-identification standards (Safe Harbor and Expert Determination), practical mechanics of working with PHI safely, auditability, access control, and documentation fit for high-stakes or regulated AI programs.

Ability to work credibly with clinicians, biomedical SMEs, research scientists, engineers, technical solutions teams, annotators, and customer stakeholders.

A bias toward clinical realism: you would rather build a smaller dataset that reflects what clinicians, reviewers, patients, or care teams actually see than a larger dataset that looks impressive on paper but fails in practice.

Degree in a relevant field such as biostatistics, epidemiology, computational biology, health informatics, computer science with a health focus, statistics, a clinical degree with quantitative training, or equivalent demonstrated experience.

Clinical credentials are not required, but candidates must be able to work credibly with clinicians, biomedical SMEs, and health AI customers; candidates with MD, RN, PharmD, MPH, PhD, or health informatics backgrounds are especially encouraged.

The expected salary range for this position is $210,000 – $240,000 USD per year, based on experience, skills, and qualifications.

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