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Rohlik is the leading Central European e-grocer operating across five countries (Czech Republic, Germany, Hungary, Austria, Romania) with over one million customers. The company delivers groceries from a selection of 20,000+ items within 15-minute windows.
You will lead the Applied Science function, reporting directly to the Group CTO. This role unifies Forecasting and Optimization into a single end-to-end decision-making ecosystem. Your team's models drive critical operational and business outcomes: inventory purchasing across warehouses, labor scheduling, product placement, and delivery routing across all five markets. When forecasts are accurate, shelves stay full and waste is minimized. When plans are right, warehouses run efficiently and orders arrive on time. Errors translate directly into shrink, overtime, capital tie-up, and missed deliveries.
Key responsibilities include:
- Owning business results from the forecasting and optimization portfolio, setting targets with cell leads and tracking financial/operational impact in production
- Defining which decisions to automate next across purchasing, fulfillment center planning, workforce scheduling, and commercial optimization
- Setting technical standards and architecture for problem framing, method selection, fast MVPs, honest evaluation/backtesting, and reliability monitoring
- Merging Forecasting and Optimization teams; setting hiring standards, developing ML engineers and applied scientists, fostering an AI-first culture
- Partnering cross-functionally with operations managers, product owners, and engineering leads on business metrics, baselines, and system integration
The environment is AI-first: engineers use coding agents (Devin, Claude Code) daily. You will have zero friction to deployment—a solid backtest is all you need to ship. You'll shape technical strategy with direct executive exposure and true ownership.
Requirements:
- 8+ years applying machine learning, forecasting, or operations research to real operational or commercial problems with clear metrics demonstrating business results
- Proven track record of working backwards from outcomes: defining target metrics first, selecting simplest effective methods, shipping fast MVPs, iterating in production
- Hands-on technical depth in production time-series/probabilistic forecasting or mathematical optimization (LP, MIP, CP, heuristics, simulation); ability to judge when simple heuristics beat complex models
- Fluency in Python and SQL; hands-on experience with production pipelines, monitoring, and backtesting; familiarity with modern forecasting stacks or solvers (Gurobi, CPLEX, OR-Tools)
- Direct experience working alongside supply chain, warehouse, or logistics operations, including time on the floor understanding problems before modeling
- Proven experience leading technical teams, hiring talent, developing engineers, translating complex model mechanics into business trade-offs for executives
- Active use of coding agents and modern AI tooling in daily workflow, driving adoption across your team
- M.S. or Ph.D. in Operations Research, Computer Science, Statistics, Applied Mathematics, or equivalent hands-on experience
Nice to have:
- Experience in e-commerce, grocery, supply chain, or logistics at scale
- Experience building or scaling a forecasting, optimization, or decision-science function from early stage
- Experience feeding probabilistic forecasts directly into optimization models
- Experience with GCP, Snowflake, BigQuery, or ML platforms such as ZenML or Vertex AI
