SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Rohlik is the leading Central European e-grocer operating across five countries (Czech Republic, Germany, Hungary, Austria, Romania) with over a million customers. The company delivers 20,000+ items within 15-minute windows, powered by continuous automated decision-making across inventory management, workforce scheduling, warehouse operations, and logistics.
In this role, you will lead the Applied Science function, reporting directly to the Group CTO. You will unify Forecasting and Optimization into a single end-to-end decision-making ecosystem, owning the business outcomes, technical strategy, and team leadership across both capabilities.
Key responsibilities include:
- Owning business results from forecasting and optimization models, setting targets and tracking financial/operational impact in production
- Defining the strategic roadmap for automation across purchasing, fulfillment center planning, workforce scheduling, and commercial optimization
- Setting technical standards for problem framing, method selection, MVP development, backtesting, and reliability monitoring
- Merging Forecasting and Optimization teams, hiring ML engineers and applied scientists, and fostering an AI-first culture
- Partnering cross-functionally with operations, product, and engineering to align on metrics and system integration
Your models directly drive availability, shrink, fulfillment costs, and working capital. You will operate in an environment with zero friction between backtesting and production deployment, with direct executive exposure and true ownership of technical strategy.
The company uses AI coding agents (Devin, Claude Code) daily and expects you to push boundaries of modern agentic workflows.
Requirements:
- 8+ years applying machine learning, forecasting, or operations research to real operational or commercial problems with demonstrated business impact
- Proven track record of working backwards from outcomes: defining metrics first, selecting simple effective methods, shipping fast MVPs, iterating in production
- Hands-on technical depth in time-series/probabilistic forecasting or mathematical optimization (LP, MIP, CP, heuristics, simulation); ability to judge when simple heuristics outperform 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 operational experience working alongside supply chain, warehouse, or logistics operations, including floor time
- Proven leadership experience: building technical teams, hiring talent, developing engineers, translating model mechanics into business trade-offs for executives
- Active use of coding agents and modern AI tooling in daily workflow
- M.S. or Ph.D. in Operations Research, Computer Science, Statistics, Applied Mathematics, or equivalent hands-on experience
Nice to have:
- E-commerce, grocery, supply chain, or logistics experience at scale
- Experience building or scaling forecasting, optimization, or decision-science functions from early stage
- Experience feeding probabilistic forecasts into optimization models
- Experience with GCP, Snowflake, BigQuery, or ML platforms (ZenML, Vertex AI)
