Responsibilities
- Define the technical vision and roadmap for a machine learning-driven pricing system, serving as a foundational component of intellectual property, with full ownership of its design and performance.
- Establish the mathematical framework for a complex pricing challenge that does not yet have a complete theoretical basis, creating a real-time system that jointly optimizes for customer conversion, financial margin, and portfolio equilibrium.
- Select and implement the most effective modelling techniques—such as reinforcement learning, stochastic programming, or classical machine learning—based on problem requirements rather than convenience or familiarity.
- Develop and deploy machine learning models from concept to production, maintaining full ownership of the data architecture and modelling pipeline using production-ready Python code.
- Ensure models are production-ready by design, minimizing reliance on external engineering teams and enabling independent execution from research to deployment.
- Address systemic imbalances through probabilistic modelling to improve risk assessment and short-term decision-making in a fast-changing operational environment.
- Serve as the primary technical communicator for the pricing ML system, translating complex functionality and decisions for commercial, product, and engineering stakeholders with clarity and technical accuracy.
Work Arrangement
Remote (Worldwide)
