Applied Healthcare Researcher

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United States
Workplace: RemoteFull timeFunction: Healthcare (Clinical, Medical, Wellness)Education: phdSkills: ["Customer focus","Independent work","Rigor","Communication","Ownership"]

Drive fast-iterating, customer-facing healthcare training data research. Partner with frontier lab and startup researchers to translate model-development goals into feasible healthcare data strategies, run feasibility and evaluation work, and build the evidence base (benchmarks, validation, error analysis) that proves dataset suitability. Collaborate cross-functionally with solutions, healthcare data partnerships, and product/engineering to scale reusable workflows and explain data limits, tradeoffs, and biases.

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5 hours ago

Applied Healthcare Researcher

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Job Summary

Drive fast-iterating, customer-facing healthcare training data research. Partner with frontier lab and startup researchers to translate model-development goals into feasible healthcare data strategies, run feasibility and evaluation work, and build the evidence base (benchmarks, validation, error analysis) that proves dataset suitability. Collaborate cross-functionally with solutions, healthcare data partnerships, and product/engineering to scale reusable workflows and explain data limits, tradeoffs, and biases.
Location: United States
Workplace: Remote
Employment Type: Full time
Job Function: Healthcare (Clinical, Medical, Wellness)
Seniority: Mid level

Key Responsibilities

  • •Serve as the primary technical and research point of contact for healthcare customer conversations, translating model-development goals into feasible data strategies.
  • •Conduct applied research and method development to demonstrate that datasets support customer training or evaluation objectives (including feasibility research).
  • •Build credible evidence bases using benchmarks, validation analyses, and error characterization, including honest assessments of dataset limitations.
  • •Evaluate dataset feasibility by assessing whether requested variables, labels, or cohort definitions are achievable and identifying proxies or alternative structures.
  • •Produce reusable research and technical collateral, and partner with product and engineering to operationalize repeatable workflows for scaling across customers.

Key Requirements

  • •Advanced degree (PhD or Master’s plus 3+ years 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 working with healthcare data (claims, EMR/EHR, clinical notes, imaging, registries, or similar), including understanding messy real-world clinical data and implications for training.
  • •Strong Python and SQL skills, able to work independently on large datasets.
  • •Experience designing evaluations (measuring data quality and representativeness) and translating ambiguous goals into defensible research plans.
Experience:Healthcare dataMachine learningLLM
Education:PhD / Doctorate
Skills:Customer focusIndependent workRigorCommunicationOwnership
Tech Stack:PythonSQLLLM-based extractionClassificationRules-based approachesFine-tuning

Company Brief

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Provides a privacy‑first platform that connects data holders with vetted AI developers to license and deliver high‑quality, multimodal training and evaluation datasets across healthcare, media, audio, and motion capture.
Industry: Data Infrastructure
Company Size: Small (11 to 50 employees)
Growth: Growth Stage Startup
Funding: Series A
Headquarters: New York City, United States
Founded: 2024
WebsiteLinkedIn