Responsibilities
- Lead full lifecycle machine learning systems for pricing, bidding, and risk decision workflows.
- Derive modeling goals from foundational principles, defining loss functions, constraints, and evaluation metrics, and deploy them as scalable production services.
- Plan and execute experiments such as A/B tests and offline validations, iterating based on well-defined performance indicators.
- Track live model behavior, diagnose performance drops, and implement improvements to maintain accuracy and reliability.
Benefits
- Work on machine learning systems actively used in production, directly shaping large-scale pricing, risk, and vendor strategies.
- Take ownership of key models and contribute to technical design and strategic direction.
- Collaborate with skilled engineers while solving intricate optimization challenges in a fast-growing financial technology environment.
