Asks for

The ad says“Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow”
The ad says“Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation”
The ad says“Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting”
The ad says“Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting”
The ad says“Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation”
The ad says“Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation”
The ad says“Strong Python and SQL, the standard optimization and ML libraries, and the ability to run your own experiments end to end”
The ad says“Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation”
The ad says“Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation”
The ad says“Strong Python and SQL, the standard optimization and ML libraries, and the ability to run your own experiments end to end”
The ad says“Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting”
The ad says“Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting”
The ad says“Strong Python and SQL, the standard optimization and ML libraries, and the ability to run your own experiments end to end”
The ad says“Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting”
The ad says“Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting”

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

Benefits

Free lunch everyday and fully-stocked microkitchens and coffee/tea bar
Team offsites, game hours, tech talks, and design sprints
Health insurance benefits (PPO / HMO / HSA, 100% of family premiums paid)
Dental / vision / wellness benefits
401k matching
Unlimited PTO
Flexible work-from-home policy for work/life balance (4 days in-office, accommodation for occasional WFH days)
Relocation assistance
Generous parental leave (4 months for the birthing parent, 3 months for a partner, 100% paid)

Preferred experience

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
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
Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow
Interest in operations collaboration and applied healthcare impact
Judgment about how uncertainty, constraints, and edge cases behave in real-world operational data
Strong Python and SQL, the standard optimization and ML libraries, and the ability to run your own experiments end to end
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

Recruitment process

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