Data Scientist, Reinforcement Learning
Tasks
- Apply safe and constrained reinforcement learning
- Benchmark reinforcement learning against traditional methods
- Build simulation environments and digital twins
- Conduct reinforcement learning research and knowledge sharing
- Design reinforce learning solutions
- Develop reinforcement learning agents
- Formulate sequential decision problems
- Integrate reinforcement learning with optimization and control systems
- Operationalize machine learning pipelines
Perks/Benefits
- Comprehensive medical, dental and vision plans
- Culture of health programs
- Disability plan
- Employee health advisory program
- Pension plan
- Savings plan
- Workplace flexibility
Skills/Tech-stack
Actor-critic | Azure Machine Learning | Azure OpenAI | Control Theory | Databricks | Decision Making | Deep Q-Learning | Digital Twins | Dynamic Programming | Gymnasium | Hierarchical reinforcement learning | Integer Programming | Linear Programming | MLOps | MLflow | Machine Learning | Markov Decision Process | Mixed Integer | Mixed-Integer Programming | Model Free Reinforcement Learning | Model-Predictive Control | Model-based reinforcement learning | Multi-Agent | Multi-Agent Reinforcement Learning | Offline Reinforcement Learning | Optimization | Policy Gradient | Policy Optimization | Predictive control | Probability | Proximal Policy Optimization | PyTorch | Python | Q-learning | Reinforcement Learning | Rllib | Safe Reinforcement Learning | Sequential decision making | Simulation Environments | Soft Actor Critic | Stable-Baselines3 | Statistics | Temporal Difference Learning | TensorFlow | Twin Delayed DDPG | Weights and Biases
Education
Roles
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