Contract Data Scientist (40 hrs/wk)

Layer Health•Boston, MA
•$40 - $70•Hybrid

About The Position

Layer Health is hiring a contract Data Scientist to work with its ML team, applying existing machine learning workflows to tailor models for specific clinical use cases. The role involves designing, building, evaluating, and iterating on models by combining data science fundamentals with modern LLM techniques to deliver accurate, production-ready solutions across new clinical domains. This is a high-velocity, high-ownership role focused on investigating model errors, making targeted improvements, and connecting model performance to real-world impact. The contract requires full-time hours.

Requirements

  • Good data science fundamentals, including knowledge of best practices for evaluating model performance and conducting error analysis.
  • Solid programming skills in Python.
  • Strong technical communication skills and the ability to thrive in a fast-paced environment.
  • A proactive mindset for identifying and solving problems and continuously improving data science capabilities.
  • An adaptable, collaborative team-oriented approach and interest in applying AI/ML to healthcare.
  • Willingness to work in a hybrid arrangement at the Boston or NYC office three days per week.

Nice To Haves

  • A proven track record of successful projects in healthcare or clinical applications.
  • Familiarity with modern applied LLM techniques and their practical implementations, such as standard prompting techniques, LLM frameworks, and structured output usage.

Responsibilities

  • Own end-to-end development of models for specific use cases, from initial prototyping through production refinement.
  • Evaluate model performance rigorously and conduct deep error analysis to identify systematic failure modes and guide improvements.
  • Ship frequent, high-quality model updates based on data, feedback, and observed edge cases.
  • Apply modern LLM techniques, including prompting strategies, structured outputs, tool use, and evaluation frameworks, to improve accuracy and robustness.
  • Contribute to metrics, reporting, and internal dashboards that track model performance and downstream impact.
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