Applied Scientist, Sprinter Health

• The ideal candidate is a scientist-engineer who reasons from first principles about uncertainty and constraints, reaches for the simplest model that works, and can move from a formulation on the whiteboard to a decision that runs in production
• Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation
• Strong Python and SQL, the standard optimization and ML libraries, and the ability to run your own experiments end to end
• Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow
• Ability to turn an ambiguous problem into a well-posed optimization or forecasting task, discover and analyze related literature, and adapt/apply those methods to our tasks
• Judgment about how uncertainty, constraints, and edge cases behave in real-world operational data
• Interest in operations collaboration and applied healthcare impact

Desirable

• We’re looking for an Applied Scientist to turn Sprinter’s hardest logistics problems into optimization models and decision systems that get the right clinician to the right patient at the right time
• Sprinter runs a two-sided operation — clinicians on one side, patients who need care at home on the other — and we must match supply to demand across large regions under complex constraints
• As an Applied Scientist, you will take ambiguous operational problems and shape them into well-posed tasks, strong baselines, and honest evaluations
• The algorithms you build will answer questions like which clinician sees which patient, in what order, given drive time, appointment windows, and clinical constraints; how many clinicians to staff in each region next month; and how long a visit will take or whether a patient is likely to cancel
• This role sits at the intersection of research and engineering, blending scientific rigor with a deployment-oriented mindset
• It also requires close cross-functional partnership with operations, product, and engineering stakeholders
• Turn ambiguous operational problems into well-posed optimization, forecasting, or simulation tasks
• Build strong baselines and improve on them efficiently, adding complexity only when the value justifies it
• Develop solutions across operations research, optimization, and machine learning, choosing the right tool for the problem
• Run careful analysis and iterate toward decisions that improve real operational outcomes — cost per visit, clinician utilization, patient access, and visits completed
• Design offline evaluations, simulated backtests, and live experiments that predict real-world operational impact
• Find the gaps between a model’s assumptions and messy operational reality before they reach production
• Choose metrics suited to stochastic, constrained, and partially observed operational systems
• Interpret and communicate results effectively to cross-functional stakeholders
• Partner with Engineering to productionize optimization and decision systems reliably
• Work with operations partners and SMEs to validate assumptions and review where decisions break down
• Explain tradeoffs, uncertainty, and limitations clearly to product and leadership

• We aim to complete the interview process between 2–3 weeks. It will usually consist of:
• Recruiter Screen (30 minutes)
• Hiring Manager Introduction (30 minutes)
• Hands-on-Keys Technical Assessment (1 hour)
• Onsite Interview: Systems Design / Technical Case Study + Research Presentation + Behavioral Interview + Lunch with the Team (4 hours)
• References