Lead Decision Intelligence Engineer - NBA
Tasks
- Build PySpark feature engineering jobs
- Build training pipelines
- Conduct policy evaluation
- Coordinate multi agent RL decisions
- Create simulation and backtesting environments
- Design RL algorithms
- Diagnose RL failure modes
- Document model behavior and limitations
- Enforce clinical eligibility constraints
- Implement feedback loop with Kafka and Databricks Delta Live Tables
- Implement offline reinforcement learning
- Implement policy gradient methods
- Implement reward design
- Instrument MLflow tracking
- Integrate model outputs with real time decisioning
- Manage model artifacts in MLflow Model Registry
- Support explainability for stakeholders
- Train reinforcement learning policies
Perks/Benefits
Skills/Tech-stack
A3C | Backtesting | Bellman Equation | CQL | Constraint Mapping | Databricks | Databricks Delta | Databricks Delta Live Tables | Decision Transformer | Deep Q-Learning | Delta Lake | Delta Live Tables | Exploration/exploitation | Gymnasium | Kafka | Linear Programming | MLflow | MLflow Model Registry | Markov Decision Process | Model Registry | Multi-Agent | Multi-Agent Reinforcement Learning | Offline Reinforcement Learning | OpenTelemetry | PPO | PettingZoo | Policy Gradient | PySpark | PyTorch | Python | Q-learning | Ray RLlib | Redis | Redis caching | Reinforcement Learning | Reward shaping | Simulation | TensorFlow | Unity Catalog
Education
N/A
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