Innodata Inc.
Applied Data Scientist, Health AI Evaluation & Datasets
Remote - United States
Sponsorship not specifiedDetected 15 days ago
PythonSQLMachine Learningscikit-learnPandasData EngineeringData ScienceLLMsRAGAgentic AIComplianceHIPAAHL7/FHIREHR/EMRICD-10FDA RegulatoryMedical DevicesResearchCommunication
About the role
- Healthcare is one of the highest-stakes domains for generative AI.
- Clinical accuracy, patient safety, regulatory compliance, health equity, auditability, and workflow fit are the bar for shipping anything real.
Responsibilities
- Partner with Applied Research Scientists and AI/ML Research Engineers to instrument datasets into evaluation and post-training pipelines, including rubric-grounded LLM-as-judge prompts, regression suites, model comparison workflows, experiment tracking, and model-improvement feedback loops.
Benefits
- calibration, hallucination on safety-critical content, refusal appropriateness, robustness under ambiguity, equity across patient subgroups, and safe handoff in agentic or workflow-integrated systems.
- reusable health-domain taxonomies, evaluation rubrics, golden datasets, clinical review playbooks, dataset quality checks, and methodology templates.
- Solid grasp of healthcare priv
- Innodata partners with foundation model labs, medical AI startups, payers, providers, pharma, and digital health companies building LLMs, multimodal systems, and AI agents for healthcare and life sciences.
- As an Applied Data Scientist, Health AI Evaluation & Datasets, you own the design, measurement quality, and clinical validity of datasets used to train, fine-tune, and evaluate health-domain models.
- 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.
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