Job Opportunities API

Group 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. Own solver strategy (Gurobi / CPLEX / OR-Tools), formulation efficiency, and solution-time guarantees suitable for interactive use. 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 recommendations derived from historical RFP and booking data. The output is not a dashboard; it is a defensible, quantified action a revenue director can take. Scientific rigor in production. Establish the methodological standard: reproducible experiments, benchmark instances, ablations against heuristic baselines, and honest measurement of realized revenue impact at customers. What ships must be defensible — to a hotel’s revenue director and to a referee. What success looks like: 3 months: A first optimization model (e.g., displacement-based price floors) validated against historical booking data and benchmarked against current heuristic practice. 12 months: The optimization core prices group business in production at multiple customers with measured GOP uplift, the insight layer is a selling point in our enterprise deals — and you are positioned to take ownership of the AI team. A body of results strong enough for an OR or revenue-management venue (e.g., INFORMS) is a welcome side effect. MSc or PhD in Operations Research, applied mathematics, industrial engineering, computer science, or a comparable quantitative field Solid grounding in mathematical optimization: LP/MILP formulation and solution techniques, and ideally stochastic or robust optimization Hands-on experience with at least one industrial solver (Gurobi, CPLEX, OR-Tools, HiGHS) beyond coursework Strong Python and the engineering discipline to ship models as maintainable production services, not notebooks tatistical modeling and forecasting competence (time series, probabilistic models, backtesting) Ability to translate a messy business problem into a well-posed formulation — and to explain the solution to a non-mathematical stakeholder Strong English (B2–C1), German (min. B2) Nice to have, not required: Background in revenue management, dynamic pricing, or network RM (familiarity with the Talluri & van Ryzin canon) Publications, or a thesi