Demand Forecasting Lead — Food Tech

Farmer’s Fridge; Chicago (IL); On-site; USD 160,000 - 170,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with immediate employer match

Paid time off

Paid sabbatical after 5 years

Employee discounts

Employee Assistance Program (EAP)

Job summary

Farmer’s Fridge is seeking a Lead Data Scientist, Demand to own and improve demand forecasting models across fridge and retail locations. You will maintain production models, scale forecasts to thousands of fridges, and translate forecasts into stocking decisions with cross‑functional partners.

You will partner with the Lead Decision Scientist and Data Engineering, owning the probabilistic forecasts and backtesting. The role is in-office in Chicago with growth into company‑level forecasting.

Qualifications

• 4-8 years building and maintaining forecasting or predictive models in production.
• Deep demand forecasting experience: time series, hierarchical, or ML-based methods (gradient boosting, state space, Bayesian, or similar).
• Comfort with count data and intermittent demand (Poisson, negative binomial, or similar).
• Experience correcting for censored demand from stockouts, or a strong grasp of why it matters.
• Strong Python and SQL. Experience with Snowflake or a similar cloud warehouse.
• Rigor in backtesting and experiment design. You know how to prove a model change actually helped.
• Track record of owning model work end to end, from problem framing through production and monitoring.
• Clear communicator. Able to explain forecast uncertainty and tradeoffs to planners and operators, and defend modeling choices to technical peers.

Responsibilities

• Own our fridge and retail demand forecasts end to end: data, features, model, validation, and production.
• Build forecasts that maximize contribution margin dollars, not forecast accuracy for its own sake. Track the impact through sell‑through, unsolds and stockouts against Channel's targets.
• Maintain and improve the models in production today. Rebuild them where a better approach clearly pays off.
• Forecast at the item, location, and day level across 2,000+ fridges and retail partner stores, where demand is often low‑volume and intermittent.
• Model the hard parts of fresh food: stockouts that hide true demand, short shelf life, new fridges ramping up, new menu items, holidays, and promotions.
• Produce probabilistic forecasts, not just point in time estimates, so our optimization models can weigh the cost of a lost sale against the cost of waste.
• Partner with the Lead Decision Scientist on the forecast outputs the solver needs. Measure how forecast changes move stocking decisions and results.
• Build backtesting and monitoring. Judge accuracy on business outcomes like sell‑through, waste, and lost sales, not just statistical error.
• Run a standing model scorecard, reviewed with Channel Strategy and Demand Planning on a set cadence. Every material model change ships with a measured before and after on sell‑through, unsolds and stockouts, translated into contribution margin per fridge per week.
• Partner with Data Engineering to run models in our production pipelines (Snowflake, dbt, Dagster) without manual babysitting.
• Work with demand planning, supply chain, and operations. Explain what the model expects, why, and how much to trust it.
• Measure the value of planner overrides. Fold the ones that consistently help into the model as explicit rules, and retire the ones that don't.
• Expand into company‑level volume and revenue forecasting as the core models mature.
• Keep a living record of every business rule in the models: what it does, where it came from, who owns it, and when it last changed.

Skills

Forecasting models

Python

SQL

Model validation

Communication

Problem solving

Attention to detail

Tools

Snowflake

dbt

Dagster

Job description

Farmer’s Fridge is seeking a Lead Data Scientist, Demand to own and improve demand forecasting models across fridge and retail locations. You will maintain production models, scale forecasts to thousands of fridges, and translate forecasts into stocking decisions with cross‑functional partners.

You will partner with the Lead Decision Scientist and Data Engineering, owning the probabilistic forecasts and backtesting. The role is in-office in Chicago with growth into company‑level forecasting.