[Remote] Senior Data Scientist

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