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Optimization Scientist Jobs (NOW HIRING)

US Mobile

Data Scientist (Cost Optimization)

Manhattan, NY · On-site

$140K - $190K/yr

Data Scientist, Cost Optimization** to own the unit economics of our network costs. You'll build the models and optimizations that decide the most cost-efficient configuration for every line on our ...

US-Mobile

Data Scientist (Cost Optimization)

Manhattan, NY · On-site

$140K - $190K/yr

Data Scientist, Cost Optimization** to own the unit economics of our network costs. You'll build the models and optimizations that decide the most cost-efficient configuration for every line on our ...

US Mobile

Data Scientist (Cost Optimization)

New York, NY · On-site

$140K - $190K/yr

Data Scientist, Cost Optimization** to own the unit economics of our network costs. You'll build the models and optimizations that decide the most cost-efficient configuration for every line on our ...

Amcor plc

Sr. Data Scientist - Optimization

Neenah, WI · On-site

Experience with optimization solvers (HiGHS, Gurobi, CPLEX) * Proven understanding of polymer science, film extrusion, or food-packaging interactions is highly preferred. #LI-onsite Our Expectations ...

Amcor

Sr. Data Scientist - Optimization

Neenah, WI · On-site

Experience with optimization solvers (HiGHS, Gurobi, CPLEX) * Proven understanding of polymer science, film extrusion, or food-packaging interactions is highly preferred. #LI-onsite OurExpectations ...

Baylor College of Medicine

Staff Scientist

Houston, TX · On-site

Conduct literature searches to identify optimal scientific protocols to address experimental questions. * Develop research techniques when necessary for specific research projects. * Coordinate the ...

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Optimization Scientist information

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$122.7K

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How much do optimization scientist jobs pay per year?

As of Sep 9, 2026, the average yearly pay for optimization scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is an optimization scientist?

An Optimization Scientist is a professional who uses mathematical models, algorithms, and data analysis techniques to improve processes, systems, or products. They work in various industries to identify inefficiencies and develop solutions that maximize performance, minimize costs, or achieve specific objectives. Their work often involves operations research, machine learning, and computer programming to solve complex problems and support decision-making. Optimization Scientists collaborate with engineers, analysts, and business leaders to implement and monitor solutions. Their goal is to help organizations operate more efficiently and effectively.

What are the key skills and qualifications needed to thrive as an optimization scientist, and why are they important?

To thrive as an Optimization Scientist, you need a solid background in mathematics, operations research, and data analysis, typically supported by an advanced degree in a quantitative field. Familiarity with optimization software (like Gurobi or CPLEX), programming languages such as Python or R, and experience with modeling tools are crucial. Strong problem-solving, communication, and collaboration skills help translate complex data into actionable business solutions. These competencies are vital for designing efficient systems and providing measurable improvements in operational performance.

How does an optimization scientist typically collaborate with cross-functional teams to implement solutions?

Optimization Scientists often work closely with data engineers, software developers, and business stakeholders to design and implement effective optimization models. They translate complex analytical findings into actionable solutions, ensuring that technical recommendations align with business goals. Regular communication and collaboration are essential, as Optimization Scientists may need to explain model assumptions, gather domain knowledge, and adjust their approaches based on feedback from other teams. This interdisciplinary teamwork not only enhances solution quality but also helps in the smooth adoption of optimization strategies across the organization.

What cities are hiring for Optimization Scientist jobs?

Cities with the most Optimization Scientist job openings:

What states have the most Optimization Scientist jobs?

States with the most job openings for Optimization Scientist jobs include:

What are popular job titles related to Optimization Scientist jobs?

For Optimization Scientist jobs, the most frequently searched job titles are:

Infographic showing various Optimization Scientist job openings in the United States as of August 2026, with employment types broken down into 88% Full Time, 9% Part Time, and 3% Contract. Highlights an 78% Physical, 5% Hybrid, and 17% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Applied Scientist, Optimization & Logistics

San Francisco, CA • On-site

Sprinter Health
Health Care and Social Assistance • 11 - 50 employees
Learn more about Sprinter Healthexternal

$160K - $220K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 24 days ago

Key responsibilities

  • Turn ambiguous operational problems into well-posed optimization, forecasting, or simulation tasks.

  • Develop solutions across operations research, optimization, and machine learning, choosing the right tool for the problem.

  • Partner with engineering to productionize optimization and decision systems reliably.

Job description

About Sprinter Health:
At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can't get to a doctor's office. For many, the ER becomes their first touchpoint with the healthcare system-driving over $300B in avoidable costs every year.
By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we've supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway.
About the Role
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. 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.
Hybrid & Office Experience
We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days.
We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most.
Lunch is provided every day, and the entire team takes an hour to eat together. It's one of the ways we stay connected outside of meetings. You'll usually find us playing a board game before getting back to work.
What you will do:
Modeling & Optimization
  • 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.
Evaluation & Scientific Rigor
  • 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.
Collaboration & Delivery
  • 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.

What you have done:
  • 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.

What gives you an edge:
  • 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.

Interview 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

What we offer:
  • 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

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