Data Scientist, Reinforcement Learning (Spring, TX, US, 77389)

ExxonMobil

Experience: 5+ yrs
Location: Spring, Texas, United States
Job type: Full-time

Required skills

Python
PyTorch
TensorFlow
Stable Baselines3
RLlib
Gymnasium

About the role

Your role on our team

Pioneer the application of reinforcement learning (RL) and sequential decision-making to high-impact challenges across ExxonMobil'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.

What you will do

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.

About you

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 TensorFlow, 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.

Your benefits

An ExxonMobil career is one designed to last. Our commitment to you runs deep: our employees grow personally and professionally, with benefits built on our core categories of health, security, finance, and life.

We offer you:

Pension Plan: Enrollment is automatic and at no cost to you. The basic benefit is a monthly annuity to be paid to you in retirement for the rest of your life.
Savings Plan: You can contribute between 6% and 20% of your pay and are encouraged to enroll right away. If you contribute at least 6% to your savings plan, the Company will contribute a 7% match.
Workplace Flexibility: We have several programs such as “Flex your Day”, providing ad-hoc flexibility around when and where you work, as well as longer-term programs such as leaves of absence and part-time work.
Comprehensive medical, dental, and vision plans.
Culture of Health: Programs and resources to support your wellbeing.
Employee Health Advisory Program: Provides confidential professional counseling for you and your family, including tools and resources promoting mental health and resiliency at no additional cost to you.
Disability Plan: Income replacement for when you cannot work due to illness or injury occurring on or off the job. Enrollment is automatic and at no cost to you.

More information on our Company’s benefits can be found at www.exxonmobilfamily.com.

Please note benefits may be changed from time to time without notice, subject to applicable law.

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About ExxonMobil

Produces and distributes oil, natural gas, and petrochemical products.

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