Note: The job is a remote job and is open to candidates in USA. Synapse Health is streamlining the durable medical equipment process and is seeking a Staff Data Scientist to enhance their data capabilities. The role involves building predictive models, optimizing processes, and mentoring other data scientists while working with complex data sets to drive impactful decisions.
Responsibilities
- Build and own models across vendor matching, order routing, and supply chain optimization, expanding into new problem areas as priorities shift
- Architect confidence thresholds and decision logic that let systems act autonomously once proven trustworthy, pushing the team's work toward agentic AI
- Quantify the counterfactual for your work: prove out impact 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 the problem — predictive ML, causal ML, reinforcement learning, operations research, or causal inference — rather than defaulting to one toolkit
- Mentor senior data scientists on the team, raising the technical bar without formal management responsibility
- Write clear technical design docs and hold your own work to a high bar
- Partner with the Director and the rest of the team to break work into quarterly, leverage-sequenced priorities
- Stay flexible as the team's scope expands into Revenue Cycle Management, Finance, and other domains
Skills
- Master's degree required in a quantitative field (Computer Science, Statistics, Data Science, Operations Research, or related)
- 7+ years in data science, with a track record as a highly independent, senior IC
- 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 products end to end, from experimentation through production, in close partnership with data engineering
- Able to write technical design docs and hold your own work to a high standard
- 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
- Offline and online reinforcement learning for sequencing decisions that improve in-flow/out-flow over time
- Operations research methods (queueing theory / Little's Law, network flow optimization, discrete event simulation) applied to supply chain or logistics
- Rigorous causal inference skills — estimating heterogeneous treatment effects and applying quasi-experimental designs like difference-in-differences and regression discontinuity
- Deep expertise in health economics — able to rigorously evaluate ROI and connect data science impact directly to business value
- Experience building agentic AI tools, with a point of view on how emerging AI capabilities could unlock future use cases beyond what's scoped today
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