Deine AufgabenGroup and MICE business is one of the last scientifically underserved problems in revenue management.
Transient pricing has decades of literature and tooling; group pricing — with its displacement effects, function-space coupling, multi-resource capacity constraints, and negotiation dynamics — remains largely heuristic across the industry.
We are building the system that changes that, and this role owns its mathematical core.
You will formulate and solve the optimization problems behind Rocket’s intelligence layer: given a group request, current bookings, forecasted transient demand, and function-space availability — what decision maximizes the hotel’s GOP? That single objective — measurable gross operating profit uplift for our customers — is the north star of everything you build.
These are genuine OR problems: mixed-integer programs, stochastic demand models, displacement cost estimation — and your formulations run in production, pricing real group business for enterprise hotel chains.
Optimization models for group pricing and capacity allocation.
Formulate the core decision problems as mathematical programs: MILP formulations for room-block and function-space allocation, displacement-cost models quantifying what a group booking crowds out, and price-recommendation logic with business guardrails as explicit constraints.
Demand forecasting and stochastic modeling.
Build the forecasting layer the optimizer consumes: transient demand forecasts by segment and stay date, group conversion probability models, cancellation and materialization estimates — with rigorous backtesting on our Databricks data platform.
Insight generation.
Build the analytical layer that tells a hotel why — counterfactual analysis (“what would GOP have been under a different pricing policy?”), what-if simulation for revenue managers, and structured reco.