Applied Scientist (Optimization & Logistics)

Join Sprinter, a healthcare startup focused on delivering care to patients at home. As an Applied Scientist, you will tackle complex logistics problems and develop optimization models and decision systems. You will work closely with cross-functional teams and have a direct impact on improving operational outcomes. Enjoy a range of benefits, including free lunch, health insurance, unlimited PTO, and a flexible work-from-home policy.

• Transform ambiguous operational problems into well-defined optimization, forecasting, or simulation tasks, and develop solutions across operations research, optimization, and machine learning.
• Build strong baselines and improve on them efficiently, adding complexity only when the value justifies it, and run careful analysis to iterate toward decisions that improve real operational outcomes.
• Partner with Engineering to productionize optimization and decision systems reliably, and work with operations partners and SMEs to validate assumptions and review where decisions break down.
• 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
• Interest in operations collaboration and applied healthcare impact
• Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation
• 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
• Strong Python and SQL, the standard optimization and ML libraries, and the ability to run your own experiments end to end
• Judgment about how uncertainty, constraints, and edge cases behave in real-world operational data
• Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow
• 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