Applied Healthcare Researcher

Responsibilities

Customer Research Partnership

• 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 healthcare data.
• Explain data limitations, tradeoffs, and potential biases to technical stakeholders.
• Answer customers’ research questions about delivered data.

Applied Research and Method Development

• Develop and evaluate methods such as fine-tuning, LLM-based extraction, classification, and rules-based approaches.
• Design and run pre-contract feasibility research to assess whether data supports model objectives.
• Build evidence through benchmarks, validation analyses, error characterization, and assessments of data limitations.
• Partner on healthcare benchmarks across modalities.

Data Feasibility and Dataset Strategy

• Evaluate whether requested variables, labels, and cohort definitions are achievable with available healthcare data.
• Identify proxy variables and alternative dataset structures.
• Analyze partner and source datasets for schema, field availability, quality, completeness, and required transformations.
• Help evaluate data partners and identify valuable datasets.

Reusable Research and Scaling

• Produce reusable research, evidence, and technical collateral.
• Identify opportunities to turn successful approaches into repeatable workflows with Product and Engineering.
• Expand proven healthcare datasets across multiple customers.

Cross-Functional Collaboration

• Work with Solutions and Forward Deployed Engineering teams on opportunities.
• Coordinate with Healthcare Data Partnerships, Product, and Engineering.

Requirements

• Advanced degree (PhD or master’s plus 3+ years of industry experience) in machine learning, computer science, biomedical informatics, epidemiology, statistics, or a related quantitative field, or equivalent applied experience.
• Hands-on experience building and evaluating ML or LLM-based systems for extraction, classification, or prediction on real-world data.
• Experience with healthcare data such as claims, EMR/EHR, clinical notes, imaging, or registries.
• Strong Python and SQL skills, with the ability to work independently against large datasets.
• Experience designing evaluations that measure data quality and dataset representativeness.
• Ability to work directly with technical stakeholders and translate ambiguous goals into concrete research plans.
• Comfort operating on customer timelines while maintaining research rigor.