Note: The job is a remote job and is open to candidates in USA. Synapse Health is focused on streamlining the durable medical equipment process and is seeking a Senior Data Scientist to contribute to their data science efforts. This role involves owning end-to-end models, building predictive analytics, and collaborating closely with data engineering to enhance operational efficiency.
Responsibilities
- Own models end to end across vendor matching, order routing, or supply chain optimization, expanding into new problem areas as priorities shift
- Build components of the confidence thresholds and decision logic that push the team's work toward agentic AI
- Quantify the impact of your own work with real numbers, not assumptions
- Ship models end to end, from experimentation through production, in close partnership with data engineering
- Apply the right technical approach to well-scoped problems — predictive ML, causal ML, reinforcement learning, or operations research — with support from senior team members on trickier judgment calls
- Write clear technical documentation for your own work
- Participate in quarterly planning, taking on clearly leverage-sequenced pieces of the roadmap
- Stay flexible as the team's scope expands into Revenue Cycle Management, Finance, and other domains
- Comfortable operating in a fast-moving, sometimes ambiguous environment
Skills
- Master's degree required in a quantitative field (Computer Science, Statistics, Data Science, Operations Research, or related)
- 4–7 years in data science
- Prior experience at an early-stage healthcare startup, with hands-on expertise in claims data and other healthcare data
- Strong technical foundation in standard predictive ML (classification, regression, forecasting)
- Strong hands-on proficiency in Python and SQL — able to write, debug, and optimize production-quality code, not just prototype in a notebook
- Understands the full software development lifecycle and works fluently with GitHub — version control, branching strategies, pull requests, and code review — as a standard part of shipping models into production
- Track record shipping ML models into production, in partnership with data engineering
- Able to write clear technical documentation for your own work
- Demonstrate effective verbal and written communication skills, including presenting findings to technical and non-technical stakeholders
- Demonstrate strong analytical and organizational skills, managing multiple workstreams and priorities
- Comfortable operating in a high-pressure, ambiguous environment where priorities shift and requirements aren't always fully defined
- Reinforcement learning for sequencing decisions over time
- Operations research methods (queueing theory, network flow optimization, discrete event simulation)
- Causal inference — difference-in-differences, regression discontinuity, or heterogeneous treatment effects
- Health economics or healthcare claims data experience
- Exposure to agentic AI tools or frameworks
Benefits
- Professional growth opportunities with compelling career paths
- Healthy work-life balance supported by flexible paid time off (PTO)
- Comprehensive benefits package, including medical, dental, vision, STD & LTD insurance for full-time team members
- 401(k) savings plan with employer matching contributions
Company Overview