Applied Data Scientist, Health AI Evaluation & Datasets
Innodata Inc.
$150,000–$175,000from the description
Jul 20, 2026
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Jul 21, 2026
What this job asks for AI summary
This role centers on designing, measuring, and validating datasets used to train and evaluate AI models in healthcare and life sciences contexts. Day-to-day work involves translating clinical use cases — such as diagnostic reasoning, note summarization, or patient-facing chatbots — into annotation schemas, sampling plans, rubrics, and quality frameworks, while ensuring clinical accuracy, fairness, and regulatory compliance. It suits a data scientist with meaningful hands-on experience in healthcare data, ML dataset construction, and LLM evaluation methodology.
Senior level · 5+ years · Remote · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 180 people nationally plausibly meet what this posting asks for (data scientists). range 35–330
Applicant volume Light — Few people clear these requirements, so an application that does clear them gets looked at. Worth applying to even if you miss a nice-to-have.
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (9)
The role sits at the intersection of data science, ML evaluation methodology, and clinical/biomedical domain expertise — it could reasonably be coded as 15-1299 (Computer Occupations, All Other) given its unique 'Applied Data Scientist for AI evaluation' framing, but 15-2051 (Data Scientists) best captures the primary day-to-day work of dataset design, statistical analysis, and ML evaluation.
The JD requires 5+ years of data science experience overall, with at least 2+ years specifically in healthcare/clinical/biomedical domains; the overall years minimum is set to 5 (the role-level gate).
Degree requirement is set to None: the JD lists relevant fields but explicitly accepts 'equivalent demonstrated experience' and does not hard-gate on a specific degree.
FHIR and HL7 are listed with 'at least passing familiarity' language, making them preferred rather than hard gates. RxNorm appears alongside the hard-required coding standards (ICD-10, CPT, SNOMED CT, LOINC) but is listed last in a series where the others are more firmly emphasized; treated as preferred.
Hugging Face is named explicitly as an example of 'modern LLM tooling' alongside 'evaluation frameworks, prompt development tools, or model APIs' — the broader capability is required, and Hugging Face is the only specifically named tool in that category.
Inter-annotator agreement metrics (Cohen/Fleiss kappa, Krippendorff alpha) are listed under the required qualifications as part of statistical literacy; named as a skill category rather than a single tool.
PHI de-identification covers both Safe Harbor and Expert Determination methods as described in the JD's HIPAA/compliance requirement block.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): inter-annotator agreement metrics.
Caller marked this a fully-remote role — scored against the national candidate pool.
Read the full posting
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