Senior Data Scientist

About the role

This is a Senior Data Scientist opportunity with a growing Series B New York health tech business that uses AI orchestration to improve revenue cycle management for healthcare providers. The role works across the full modelling lifecycle, from exploratory analysis through to production, on real claims data, with strong ownership of the work and direct exposure to product and engineering. You’ll build and deploy machine learning models for the company’s core analytics and intelligence features, run exploratory analysis on large claims datasets to find patterns and anomalies, and work with engineering to integrate models and data products into production. You’ll monitor model performance and iterate, share findings with product and customer-facing teams, and contribute to pipeline and warehouse work where needed. The stack includes Python, SQL, scikit-learn, XGBoost, AWS, SQLMesh, Temporal and MLflow. The ideal candidate will have 8+ years in data science or applied machine learning with a track record of taking models to production. Strong Python and SQL, a solid grounding in statistics and ML fundamentals, cloud experience (AWS preferred) and MLOps practice including experiment tracking and model monitoring are expected. Experience with EHR or revenue cycle data, anomaly detection or recommendation systems would be advantageous. This is a hybrid position in New York City, suited to a senior scientist who wants to ship models rather than only prototype them. We're looking for someone curious, pragmatic, and eager to work across the stack with a small senior team. You'll own end-to-end delivery of major features, influence architecture, and mentor engineers.

What you'll do

• Ship customer-facing features on a weekly cadence.
• Partner with design and product on scoping and trade-offs.
• Drive technical quality through review, testing, and docs.
• Mentor one or two engineers on your team.

Requirements