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Salary: USD 165,000 - 175,000 / annual
Farmer's Fridge is introducing optimization and decision science as a new discipline, and you'll be the first person to own this capability. The company operates a network of fresh food vending machines and pantry solutions across high-traffic locations like airports, hospitals, universities, and office buildings nationwide.
In this role, you'll own the solver stack (Gurobi) and take an existing route optimization and inventory allocation model from built to fully rolled out across the organization. You'll partner with planning and operations stakeholders to define success benchmarks and rollout criteria. Beyond the initial rollout, you'll build new decision models across the supply chain planning process—inventory, production, fulfillment, and network decisions—wherever the company is currently running on manual planning or heuristics.
You'll work in the NextMV platform to develop, test, and deploy optimization models into production. A core part of the role is translating ambiguous planning problems—constraints, tradeoffs, objectives—into solvable mathematical formulations. You'll collaborate with the data science team on model inputs like demand forecasts and feature data, but you won't own the predictive layer yourself. You'll communicate model logic, tradeoffs, and results in plain language to non-technical planning and operations stakeholders, explaining solver decisions as often as you're building them.
You'll write production-quality Python and SQL, picking up additional languages as needed. You're also responsible for setting the technical bar for how decision science work gets built, tested, and documented at Farmer's Fridge—establishing the standard rather than inheriting one.
You bring 4–10 years of experience building decision science or optimization models in production environments. You have hands-on experience with mathematical solvers (Gurobi, FICO Xpress, CPLEX, OR-Tools, or similar) and can ramp quickly on the NextMV platform. You're strong in Python and SQL, comfortable formulating real business problems as linear programs, mixed-integer programs, or constraint satisfaction problems. You communicate well with both technical and non-technical audiences, have a track record as an individual contributor owning modeling work end-to-end, and thrive in fast-paced, ambiguous environments. Familiarity with AI tools in your workflow is expected. You're based in Chicago or willing to relocate, as this is an in-office role.
