Applied Scientist, Optimization & Logistics

Sprinter Health • San Francisco, CA

13d • Hybrid

About The Position

Sprinter Health is seeking an Applied Scientist to develop optimization models and decision systems for complex logistics challenges. The role involves transforming ambiguous operational problems into well-posed tasks, building strong baselines, and conducting honest evaluations. The algorithms developed will address critical questions such as clinician-patient matching, staffing levels, and visit duration prediction. This position requires a blend of scientific rigor and a deployment-oriented mindset, with close collaboration across operations, product, and engineering teams. The ideal candidate is a scientist-engineer who can reason from first principles, apply the simplest effective models, and transition solutions from concept to production.

Requirements

• 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.

Nice To Haves

• MS or PhD in operations research, industrial engineering, computer science, applied math, statistics, machine learning, or a related quantitative field; exceptional applied experience can substitute.
• Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting.
• Experience shipping optimization or decision systems that reached production and had material real-world impact.
• Hands-on experience with supply-and-demand matching in a marketplace, dispatch, or field-operations setting.
• Fluency deciding when an exact optimization approach beats a heuristic or learned one, and vice versa.

Responsibilities

• 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.

Benefits

• Meaningful pre-IPO equity
• Medical, dental, and vision plans 100% paid for you and your dependents
• Flexible PTO + 10 paid holidays per year
• 401(k) with match
• 16-week parental leave policy for birthing parent, 8 weeks for all other parents
• HSA + FSA contributions
• Life insurance, plus short and long-term disability coverage
• Free daily lunch in-office
• Annual learning stipend

What This Job Offers

• Job Type: Full-time
• Career Level: Senior