- Abstract away the complexities behind the deployment and orchestration of a large number of forecasting workflows
- Integrate with data and compute infrastructure to optimize resource utilization and performance
- Implement automated testing and monitoring for ML models in production
- Maintain and iterate on our model registry and experiment tracking
- Co-design frameworks that support model experimentation, hyperparameter tuning, training, and deployment
- Partner with our Data Services team to incrementally improve our feature store
- Collaborate closely with data scientists to understand new model requirements
- Collaborate with the Science Platform Simulation team to incorporate forecasting into physical and portfolio asset optimizations