Applied Healthcare Researcher

The role

Applied Healthcare Researchers will work within DataLab to solve model-development problems for AI researchers. You will determine which healthcare data supports customer objectives and conduct research to demonstrate its effectiveness.

What you will do

• Serve as the primary technical and research point of contact for healthcare customer conversations.
• Translate model-development goals into concrete, feasible data strategies.
• Help customers scope opportunities and identify high-value data.
• Explain data limitations, tradeoffs, and potential biases to stakeholders.
• Answer research questions customers raise about provided data.
• Develop and evaluate methods like fine-tuning, LLM-based extraction, and classification.
• Design and run feasibility research pre-contract.
• Build an evidence base including benchmarks and validation analyses.
• Evaluate whether requested variables, labels, or cohort definitions are achievable.
• Analyze partner and source datasets for schema, quality, and completeness.

Required qualifications

• Advanced degree (PhD or Master's plus 2+ years industry experience) in machine learning, computer science, biomedical informatics, epidemiology, statistics, or a related quantitative field — or equiva
• Hands-on experience building and evaluating ML or LLM-based systems for extraction, classification, or prediction on real-world data.
• Experience working with healthcare data: claims, EMR/EHR, clinical notes, imaging, registries, or similar.
• Strong Python and SQL.
• Experience designing evaluations measuring data quality and dataset representativeness.
• Ability to work directly with technical stakeholders and translate ambiguous goals into research plans.
• Comfort operating on a customer's timeline.

Nice to have

• Energized by working directly with customers and other researchers as peers.
• Moves fast on messy, real-world problems.
• Rigorous about what the data can and cannot support.
• Enjoys the full arc from scoping to showing results.
• Thinks about leverage by building reusable versions.

About the company

Protege is building a platform to facilitate the secure, efficient, and privacy-centric exchange of AI training data. The company is a lean, fast-moving team of builders focused on solving AI's data problem.