Applied Data Scientist, Health AI Evaluation & Datasets

Scope of 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. 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. You bring clinical or biomedical fluency and data science rigor: you can read a clinical guideline, payer policy, medical literature artifact, or patient communication workflow; translate it into a measurable dataset and evaluation plan; and defend the methodology to sophisticated clinical, data science, and ML stakeholders.

You will work in a tight pod with a Technical Solutions Architect, Applied Research Scientist, AI/ML Research Engineer, and Language Data Scientists. Your role is to make sure the data, rubrics, review workflows, and measurement evidence are clinically realistic, statistically defensible, compliant, and useful for evaluation and post-training.

What You’ll Own

• Translate customer goals — such as improving differential diagnosis, evaluating a clinical note summarizer, testing a RAG-based medical literature assistant, or creating preference data for patient-facing chatbots — into dataset specifications, taxonomies, rubrics, sampling plans, and acceptance criteria.
• Make multimodal health AI a core focus: design training and evaluation datasets across clinical text, medical images, waveforms, structured EHR data, claims, trial data, medical literature, patient communications, payer policies, drug information, and other clinical artifacts, as well as use cases such as clinical reasoning, medical QA, note summarization, medical coding, patient communication, utilization management, and literature synthesis.