Director of Data Science and Analytics

Who We Are: 

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

Since 2016, with decades of industry and leadership experience, we've delivered tech-based solutions that help our partners to modernize operations, improve coordination, and reduce administrative burdens. By taking on operational and financial complexity, we're redefining how DME works for providers, prescribers, and patients. We are proud to offer work that matters, on a mission that matters.  

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

What We Need:  

The Director of Data Science and Analytics reports directly to the SVP of Data, Analytics, and AI. Our operations team processes tens of thousands of DME orders every day, often relying on individual judgment to make routing and vendor decisions in the moment. We've now processed millions of orders overall — and we're at an inflection point where that scale of data lets us build a real prediction engine to support and strengthen those decisions, not just react order by order. 

We want to build differentiated technology here, not just adopt what's off the shelf, and we want to prove it: measuring the pre/post impact of introducing this technology into our operations supply chain — on customer experience, cost, and quality — so every improvement is grounded in evidence, not assumption. This role owns both the intelligence and the economic evaluation behind that engine, built around three core problems: 

• Vendor matching — deciding which vendor fulfills each incoming order, optimizing for patient experience, delivery speed, and cost. 
• Order routing — examining how orders move from creation to delivery to identify the most efficient path, and routing new orders to maximize that efficiency. This includes flagging orders at risk of delay based on historical patterns across the variables that actually drive outcomes — DME equipment type, geography, supplier, and processing team. 
• Supply chain optimization — finding the root-cause bottlenecks across in-flow and out-flow and quantifying the counterfactual: if we made this change, how many more orders would we have processed? Every recommendation comes with a number attached, not just a hunch. 

These three problems anchor the roadmap today, but the mandate extends further — this role also owns our data science work in Revenue Cycle Management and Finance initiatives, including anomaly detection, and other domains as Synapse's data science footprint grows. 

From there, phase two takes this into agentic AI — evolving the system from one that recommends analyzed actions to one that automates them directly across the supply chain. This is a player-coach role: you'll be building models yourself while leading the team that builds the rest. 

 What You Will Do:  

• Anchor the roadmap to the P&L before writing a single model. Quantify the actual cost of a bad vendor match, a mis-routed order, and network bottlenecks against our capitated rate — so every project you take on has a dollar figure attached before it starts.  
• Break the roadmap into quarters, sequenced by leverage, not by ease. Turn vendor matching, order routing, and supply chain optimization into a quarter-by-quarter plan across the team— starting with whichever slice proves value fastest, then building toward the harder problems.  
• Build the measurement infrastructure alongside the models, not after. Stand up the pre/post and counterfactual framework (holdouts, experiment design) as part of each build, so every recommendation ships with proof of impact on cost, quality, and customer experience — not a claim you have to retrofit later.  
• Ship the first real win in your first 90 days. Pick the highest-leverage, fastest-to-prove piece of the roadmap and get it live with a measured before/after result — this is what earns the team credibility to take on the bigger problems.  
• Build the operating rhythm — the execution wheel. Put in place the sprint cadence, prioritization process, and delivery tracking that make the team's output predictable quarter over quarter, not just when you're personally driving it.  
• Operate as a technical IC on design work — personally write and review technical design docs, document decisions clearly, and make that documentation visible across the team so the roadmap isn't dependent on any one person's tribal knowledge 
• Build an early-stage startup culture on the team. Set the tone for scrappiness, ownership, and speed — a team that ships and iterates, stays proactive in ambiguous situations, and moves work forward despite uncertainty. 
• Lead and grow the team of data scientists. Hire, coach, and hold the team to a real delivery bar — while staying hands-on to build models yourself, not just review.  
• Own the data science roadmap across Operations, Revenue Cycle Management, and Finance initiatives like anomaly detection — ship the models, get them adopted, and iterate based on what the data shows post-launch. 
• Manage up in numbers. Report to the SVP — and the exec team when needed — in terms of dollars saved, orders recovered, and waste reduced, not narrative updates on "how the model is going."  
• Partner with product on the roadmap. Make sure the data science roadmap and the product roadmap are pulling in the same direction — data science work should show up as capability the product can ship, not a parallel track.  
• Push into phase two: agentic AI. Once vendor matching and routing are trusted and proven, evolve the system from recommending actions to automating them directly — architecting confidence thresholds and decision logic from day one so this transition doesn't require a rebuild. 
• Partner with Engineering on the data foundation. Work with the Director of Engineering on Snowflake and the broader data architecture, so the platform can actually support what the team is building. 

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: 

• Education — Master's degree required in a quantitative field (Computer Science, Statistics, Data Science, Operations Research, or related)  
• Experience — 8+ years in data science, including 3+ years directly managing a team of data scientists, including senior/staff-level resources  
• Prior experience at an early-stage healthcare startup, with deep, hands-on expertise in claims data and other healthcare data, including health outcome measurements  
• 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, review the team's work at a high standard, and organize the team's structure around the roadmap  
• Experience managing senior technical resources, not just junior ICs  
• Demonstrate effective verbal and written communication skills, including presenting to executive stakeholders  
• Demonstrate strong analytical and organizational skills, managing multiple workstreams and quarterly priorities broken into bi-weekly delivery cadences  
• Able to personally write, review, and document technical design docs — and make that documentation visible and accessible across the team, not siloed in your own head or a personal repo. 
• Comfortable operating in a high-pressure, ambiguous environment where priorities shift and requirements aren't always fully defined  

What Sets You Apart:  

Candidates are expected to have hands-on experience in some — not necessarily all — of these areas, along with the ability to quickly learn new ones: 

• 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 

What Sets Us Apart:  

Work is a part of life, but at Synapse Health, we believe it should be meaningful and enjoyable. We’re committed to helping our team members thrive personally and professionally, which is why our benefits include: 

• 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