Decision Intelligence Engineer - Next Best Action
Tasks
- Apply constraint mapping for clinical eligibility
- Build batch scoring for eligible members
- Build distributed RL training workflows
- Deploy models to production with MLflow
- Design reinforcement learning algorithms
- Develop reward design and reward shaping
- Document model behavior for compliance and clinical stakeholders
- Ensure integration with real time decisioning hot path
- Evaluate policies with simulation and backtesting
- Implement automated evaluation gates
- Implement feature engineering pipelines
- Implement feedback loop using Kafka to Databricks
- Implement multi-agent reinforcement learning
- Instrument training runs with MLflow tracking
- Maintain data lineage with Databricks Unity Catalog
- Manage model artifacts and versioning in MLflow Model Registry
- Monitor training pipeline observability with OpenTelemetry
- Train reinforcement learning policy models
- Write production quality PySpark feature engineering jobs
Perks/Benefits
- 401k retirement savings plan
- Medical, dental, and vision benefits
- Occasional travel
- Remote work
- Time off
Skills/Tech-stack
A3C | Backtesting | Bellman Equation | Conservative Q Learning | Constraint Mapping | Databricks | Databricks Delta | Databricks Delta Live Tables | Decision Transformer | Deep Q-Learning | Deep learning | Delta Lake | Delta Live Tables | Exploration/exploitation | Gymnasium | Kafka | Kubernetes | MLOps | MLflow | MLflow Model Registry | Markov Decision Process | Model Registry | Offline Reinforcement Learning | PettingZoo | Policy Gradient | Policy Optimization | Proximal Policy Optimization | PySpark | PyTorch | Python | Q-learning | Ray RLlib | Redis | Reinforcement Learning | Reward shaping | Simulation | TensorFlow
Education
N/A
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