Applied Data Scientist, Health AI Evaluation & Datasets

Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

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