Head of Applied Science

Rohlik is the leading Central European e-grocer. More than a million customers shop with us across Rohlik.cz, Knuspr.de, Kifli.hu, Gurkerl.at, and Sezamo.ro, choosing from over 20,000 items delivered within hours in 15-minute windows.

Behind every order is a continuous stream of automated decisions: how much inventory to buy for each warehouse, how many pickers to schedule on Thursday morning, where in the warehouse a product should sit, and which van carries which bags. Our models make these choices every day across five countries. When a forecast is accurate, shelves stay full and waste is minimized.

When a plan is right, warehouses run on exact labor needs and orders arrive precisely in the customer’s chosen window. Get it wrong, and it immediately translates into shrink, overtime, tied-up capital, and missed customer deliveries.

Why this role is exciting

• Direct operational and business impact: Your team’s models directly drive availability, shrink, fulfillment costs, and working capital across all five markets.
• True ownership with zero friction: We operate automated production pipelines with no committee between your team and deployment—a solid backtest is all you need to ship.
• Unification of two core capabilities: You will bring Forecasting and Optimization into a single function, building an end-to-end decision-making ecosystem from the ground up.
• AI-first environment: Our engineers use AI coding agents (Devin, Claude Code) daily, and we expect you to push the boundaries of modern agentic workflows and tooling.
• Direct executive exposure: Reporting directly to the Group CTO, you will have the mandate and space to shape technical strategy and execution.

What you will own and deliver

• Business outcomes: Own the business results delivered by the forecasting and optimization portfolio, setting targets with cell leads and tracking financial and operational impact in production.
• Strategic roadmap & prioritization: Define which decisions to automate next across purchasing, fulfillment center planning, workforce scheduling, and commercial optimization (promotions, markdowns, demand shaping).
• Technical standards & architecture: Set the bar for problem framing, method selection, fast production MVPs, honest evaluation/backtesting against business metrics, and reliability monitoring.
• Function unification & leadership: Merge Forecasting and Optimization into one high-performing team; set hiring standards, develop ML engineers and applied scientists, and foster an AI-first way of working.
• Cross-functional partnership: Partner closely with cell leads, operations managers, product owners, and engineering leads to align on business metrics, baselines, and seamless system integration.

What we are looking for

• Track record of impact: 8+ years applying machine learning, forecasting, or operations research to real operational or commercial problems, with clear metrics demonstrating business results.
• Works backwards from outcomes: A proven habit of defining target metrics first, selecting the simplest effective method, shipping fast MVPs, and iterating in production.
• Hands-on technical depth: Experience building and shipping production models in time-series/probabilistic forecasting or mathematical optimization (LP, MIP, CP, heuristics, simulation), with enough depth to review models and judge when simple heuristics beat complex models.
• Technical skills: 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).
• Operational fluency: Experience working directly alongside supply chain, warehouse, or logistics operations, including spending time on the floor to understand problems before framing models.
• Leadership & communication: Proven experience leading technical teams, hiring top talent, developing engineers, and translating complex model mechanics into business trade-offs for executives.
• AI-first mindset: Active use of coding agents and modern AI tooling in your daily workflow, driving the same approach across your team.
• Quantitative background: 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 an early stage.
• Experience feeding probabilistic forecasts directly into optimization models.
• Experience with GCP, Snowflake, or BigQuery, and ML platforms such as ZenML or Vertex AI.

What’s in it for you

• Fair pay, transparent, stable compensation A strong fixed salary with clear rules. No bonus politics. Great work is rewarded openly and fairly.
• Good food options, fresh, every day Enjoy subsidised meals/fresh food options in the office and regular refreshments like drinks, fruit, and snacks that keep you energized.
• Life beyond work, hybrid work & real flexibility Three days in the office and the rest where it works best for you. Take time off when you need it with paid vacation days above statutory and flexible scheduling.
• Grow with Knuspr every day - we move fast, and so can you. Learn, experiment, and take ownership from day one. Every challenge is a chance to grow.
• Recognition & community - Your impact is seen and appreciated. You can celebrate colleagues through Bonusly peer-recognition credits and feel supported by a team that works hard and wins together.
• Health & wellbeing - Access to our UNIQA private insurance option and supportive wellbeing culture, keeping you healthy inside and outside of work.
• Mobility & convenience - Company car available for selected management-level roles and fuel support for regular WH travel.
• Premium membership - Free Knuspr Premium membership with exclusive benefits.
• Referral rewards - Recommend great people and get rewarded through our group referral program.
• Team spirit - We organize team events, celebrations, and moments to connect because work is better when we win together.

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