Principal Machine Learning Engineer - Revenue Optimisation
Skills
ModelingOperations ResearchOptimizationPortfolio Risk ManagementPower TradingProbabilityReinforcement
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Deep experience building ML systems for pricing revenue optimisation or real-time decision-making
- Track record of models that reached production and improved commercial metrics
- Strong foundation in stochastic optimisation and probabilistic modelling
- Ability to formulate ambiguous business problems mathematically before choosing a tool
- First-principles judgement across stochastic programming reinforcement learning classical ML and heuristics
- Production-grade Python and high code quality
- Ability to carry models from formulation to deployment without being blocked by engineering dependencies
- Senior technical leadership with cross-functional influence and clear stakeholder communication
Nice to have
- Real-time pricing at scale
- Energy markets knowledge
- PhD depth
- Solver and PyTorch judgement
- Causal inference
- Applied RL
Day to day
- Own the technical direction for pricing ML and define the roadmap for the pricing engine
- Formulate and solve a dynamic real-time pricing problem that optimises signing probability portfolio balance and margin
- Build ship and operate production-grade models and data pipelines in Python with deployment in mind
- Develop probabilistic models to improve risk management and short-term balancing decisions in a highly dynamic environment
- Act as the voice of pricing ML across commercial product and engineering teams explaining model behaviour clearly and precisely
Hiring process
- First call with Talent Team
- Behaviour interview with Rosso GM and hiring manager
- Technical interview with the team
- Culture-add interview with stakeholders