Data Scientist

About us

Layer Health is an AI startup spun out of MIT. We leverage LLMs to vastly improve the value and impact that can be unlocked from medical record data – building the “AI layer” for healthcare.

We know that all organizations in the healthcare ecosystem struggle to extract and synthesize insights from medical records at scale. Our AI layer radically speeds up and improves the quality of information gleaned from these records to power a wide range of impactful use cases– from research to quality measurement to clinical care pathways. Our team consists of world-class experts across machine learning, LLMs, medicine, engineering, and commercial execution. We are a Series A company headquartered in Boston, with offices in both Boston and NYC.

We’re seeking outstanding hires to join our team as early members. This is an opportunity to contribute to a high-impact, collaborative, mission-driven team, and help define the next stage of growth for Layer Health. Together, we will create the AI layer that will redefine healthcare for the better.

Job Description

We’re hiring an exceptional data scientist to join our team (Boston or NYC office). In this role, you will be responsible for advancing our applied ML and data science capabilities for healthcare. 

You can expect to:

• Develop, validate, and measure the impact of large language model (LLM) and other ML-based approaches for chart abstraction from both unstructured and structured healthcare data.
• Apply and rigorously evaluate cutting-edge methods to solve real clinical problems.
• Work hand-in-hand with our customers to best leverage Layer Health’s platform and supercharge their workflows. 
• Stay up-to-date on the latest in applied NLP and generative AI techniques and proactively explore these technologies where applicable.
• Establish and enforce best practices for data science and analytics.
• Cultivate and foster a robust and thoughtful data science and product culture that drives the company forward.

We look for:

• 2-3 years of professional experience in data science (a proven track record of successful projects in healthcare or clinical applications is a bonus, but not required).
• Strong programming skills in Python, and fluency with modern data science and ML/NLP libraries (PyTorch, Tensorflow, HuggingFace, etc.). 
• Deep fluency and instincts for data manipulation, treatment, and evaluation, with the ability to wrangle large, complex datasets efficiently and methodically.
• Familiarity with modern applied LLM techniques and their practical implementations (any experience using these techniques is a bonus).
• Proactive mindset to identify and solve problems, continuously improving our data science capabilities.
• A strong communicator who thrives in a customer-focused, fast-paced environment.
• An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML, alongside an awesome team. 
• We are a Boston-based company, and expect engineers to meet regularly in-person in Boston (engineers from Boston, NYC, or east coast are welcome).

Join us and help us transform healthcare with AI. Layer Health is committed to foster an environment of inclusion that is free from discrimination.  We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law. 

Expected compensation range for this role is $135,000-190,000 for Boston and NYC-based candidates; range may vary for candidates outside of the Boston/NYC metro area. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made.