Customer Research Partnership
You will be the research partner to AI researchers at frontier labs and startups who are working on healthcare problems.
• Serve as the primary technical and research point of contact for healthcare customer conversations.
• Translate a lab’s model-development goals into concrete, feasible data strategies.
• Help customers scope opportunities and identify the highest-value data available to them.
• Explain data limitations, tradeoffs, and potential biases to technically sophisticated stakeholders while grounding conversations in what real-world data actually looks like.
• After delivery, answer the research questions customers raise about the data we provided. Delivery is not the end of the relationship.
Applied Research & Method Development
Curating the right data product is a research problem, and you’ll own solving it.
• Develop and evaluate methods — fine-tuning, LLM-based extraction, classification, rules-based approaches, or whatever the problem calls for — to demonstrate that a dataset can support a customer’s training or evaluation objective.
• Design and run feasibility research pre-contract: can this data support this model objective, at what quality, with what caveats.
• Build the evidence base that makes a data strategy credible — benchmarks, validation analyses, error characterization, and honest assessments of where the data falls short.
• Partner with the Assessments team on healthcare benchmarks across modalities.
Data Feasibility & Dataset Strategy
• Evaluate whether requested variables, labels, or cohort definitions are achievable with available healthcare data.
• Identify proxy variables or alternative dataset structures when the ideal variable doesn’t exist.
• Analyze partner and source datasets — schema, field availability, quality, completeness, and required transformations.
• Contribute to our point of view on which healthcare data matters most for which modality and which stage of model development.
• Help evaluate new data partners and identify datasets worth acquiring before a customer asks for them.
Reusable Research & Scaling
• Produce reusable research, evidence, and technical collateral rather than starting from scratch for each opportunity.
• Identify where a successful one-off approach should become a repeatable workflow, and work with Product and Engineering to operationalize it.
• Help expand proven healthcare datasets across multiple customers instead of selling them once.
Cross-Functional Collaboration
• Work with Solutions and FDEs from the beginning of an opportunity.
• Coordinate with Healthcare Data Partnerships on sourcing and with Product and Engineering on tooling.