Data Scientist, Reinforcement Learning

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Pioneer the application of reinforcement learning (RL) and sequential decision-making to high-impact challenges across Exxon Mobil's upstream, downstream, and commercial operations.

Collaborate with engineers, scientists, and business stakeholders to turn complex operational and planning problems into deployable, production-grade RL solutions.

Advance the organization's capabilities in reinforcement learning, decision optimization, and autonomous control as part of the Modeling, Optimization, and Data Science (MODS) team.

• Design, develop, and deploy reinforcement learning solutions for real-world energy applications such as production optimization, process control, supply chain scheduling, drilling optimization, and resource allocation.
• Formulate sequential decision problems by defining state spaces, action spaces, reward structures, transition dynamics, and operational constraints with domain experts.
• Develop RL agents using model-free methods (e.g., PPO, SAC, TD3, DQN where appropriate) and model-based approaches, selecting methods based on problem requirements, safety, and data availability.
• Build and use simulation environments and digital twins for offline training, policy evaluation, and validation before real-world deployment.
• Apply safe and constrained RL techniques to ensure agents operate within operational and safety limits.
• Integrate RL solutions with existing optimization, simulation, and control systems across real-time and planning use cases.
• Partner with data scientists and ML engineers to operationalize solutions, including training pipelines, monitoring, retraining, and performance tracking.
• Benchmark RL against traditional methods such as LP, MIP, heuristic search, MPC, and stochastic optimization to identify best-fit approaches.
• Stay current with advances in offline RL, safe RL, multi-agent RL, hierarchical RL, and model-based RL.
• Share knowledge, publish findings where appropriate, and mentor peers on RL best practices.

Desired

Skills:

• Experienced AI/ML professional with strong expertise in reinforcement learning, sequential decision-making, optimization, and real-world deployment.
• 5+ years of experience in AI/ML, optimization, or related fields, including at least 2 years in reinforcement learning, sequential decision-making, or optimal control.
• Master's or PhD in Computer Science, Machine Learning, Operations Research, Control Theory, Robotics, Applied Mathematics, Engineering, or a related quantitative field.
• Deep understanding of RL fundamentals, including MDPs, dynamic programming, temporal-difference learning, policy gradients, and actor-critic methods.
• Proven experience building RL systems end-to-end, from environment and reward design through training, evaluation, and deployment.
• Experience with simulation environments, digital twins, or system models.
• Strong background in statistics, probability, optimization, control theory, and algorithm design.
• Proficiency in Python, PyTorch and/or Tensor Flow, plus RL tools such as Stable Baselines3, RLlib, and Gymnasium.
• Strong communication and collaboration skills, including the ability to explain technical concepts to non-technical stakeholders.

Preferred

Skills:

• Experience applying RL or decision optimization in industrial domains such as process control, robotics, autonomous systems, supply chain, energy systems, or operations research.
• Familiarity with offline (batch) RL, safe RL, and multi-agent RL.
• Knowledge of model-based RL, MPC, and hybrid RL-control approaches.
• Understanding of classical optimization methods and how RL complements them.
• Experience with physics-informed or hybrid mechanistic/ML modeling and domain-informed reward or constraint design.
• Familiarity with platforms such as Azure ML, Azure OpenAI, Databricks, and MLOps tools such as MLflow or Weights & Biases.
• Experience in the energy industry or other asset-intensive, safety-critical sectors.

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