Senior Data Scientist

Synapse Health; United States; On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Professional growth opportunities

Flexible PTO

Medical, dental, vision, STD & LTD

401(k) with employer match

Job summary

Synapse Health is seeking a Sr. Data Scientist to own and ship predictive models for DME operations. You will work across vendor matching, order routing, and supply chain optimization, collaborating with data engineering and product teams to deliver measurable impact.

You will leverage ML, causal ML, and RL techniques, communicate findings clearly, and help shape roadmaps in a fast-paced, startup healthcare environment.

Qualifications

• Master's degree in a quantitative field (CS, Statistics, DS, OR, or related)
• 4–7 years in data science
• Experience at an early-stage healthcare startup with claims data
• Strong foundation in predictive ML (classification, regression, forecasting)
• Proficiency in Python and SQL—production-quality code
• Familiar with the full SDLC and GitHub workflows (PRs, reviews)
• Track record shipping ML models into production with data engineering
• Ability to write clear technical documentation
• Strong verbal and written communication; able to present to diverse stakeholders
• Strong analytical and organizational skills; comfortable with ambiguity and shifting priorities

Responsibilities

• Own models end to end across vendor matching, route predictions, and supply chain optimization
• Develop components of confidence thresholds and decision logic for agentic AI
• Quantify impact of your work with real numbers
• Ship models end to end from experimentation to production with data engineering
• Apply predictive ML, causal ML, RL, or OR to well-scoped problems with senior support
• Write clear technical documentation for your work
• Participate in quarterly planning and align with roadmap priorities
• Stay flexible as scope expands into Revenue Cycle Management and Finance
• Operate effectively in a fast-moving, sometimes ambiguous environment

Skills

Python

SQL

Predictive ML

Data Science

GitHub

Documentation

Communication

Reinforcement learning

Education

Master's degree in quantitative field

Tools

GitHub

Job description

Who We Are

At Synapse Health,we'restreamlining the durable medical equipment (DME) process.We manage intake, documentation, routing, claims, billing, and patient support. Our model reshapes how DME is delivered andexperienced.

Since2016,with decades of industry and leadership experience,we'vedelivered tech-based solutions that help our partners to modernize operations, improve coordination, and reduce administrative burdens. By taking on operational and financial complexity,we'reredefining how DME works for providers, prescribers, and patients.We are proud to offerwork that matters, on a mission thatmatters.

Learn more at SynapseHealth.com and on Synapse Health’s LinkedIn.

What We Need:

The Sr. Data Scientist reports directly to the Director of Data Science and Analytics (or a Staff Data Scientist, depending on team structure). Our operations team processes tens of thousands of DME orders every day, and we've now processed millions of orders overall — giving us the data to build real prediction engines rather than just react order by order. This role works generally independently and collaboratively, contributing to the moderately complex pieces of the roadmap: you own well-scoped problems end to end, but you're not expected to define the technical strategy for an entire domain the way a Staff Data Scientist would.

The Problems You'll Contribute To Include

• Vendor matching — build and iterate on models that decide which vendor fulfills each incoming order
• Order routing — build predictive models that flag orders at risk of delay based on historical patterns
• Supply chain optimization — apply the frameworks and methods a Staff Data Scientist or the Director has set up to find and quantify specific bottlenecks
• Agentic AI — implement and test components of the decision logic that more senior team members are architecting

What You Will Do:

• 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

Note: These responsibilities reflect the general nature and scope of the role but are not exhaustive. Responsibilities may evolve to meet changing business needs.

What You Have:

At Synapse Health, we’ve intentionally built a culture rooted in kindness, collaboration, and creativity, qualities we consider essential for every team member. Additional requirements include:

Requirements

• Education — Master's degree required in a quantitative field (Computer Science, Statistics, Data Science, Operations Research, or related)
• Experience — 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

What Sets You Apart:

• 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

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