Nuclear Engineering Data Scientist in , us

Position Overview

As our Nuclear Engineering Data Scientist, you will leverage advanced machine learning techniques, hybrid modeling approaches, and uncertainty quantification to tackle complex problems in nuclear engineering and related domains. This role is perfect for someone with a strong research background, experience in integrating machine learning models into engineering workflows, and a passion for applying cutting‑edge AI technologies to real‑world challenges in the nuclear industry.

Location

This position is based on‑site in Lynchburg, VA at the Advanced Technologies Office.

Your Day to Day

• Develop and deploy machine learning models, including deep neural networks (DNNs), convolutional neural networks (CNNs), and Bayesian neural networks (BNNs), to solve nuclear engineering problems.
• Design hybrid modeling frameworks that combine physics‑based and data‑driven approaches for applications such as critical heat flux prediction and reactor physics simulations.
• Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems.
• Optimize nuclear engineering processes using advanced AI methodologies, including transfer learning and generative models.
• Collaborate with cross‑functional teams to integrate ML models into existing nuclear engineering tools.
• Lead efforts in data augmentation, synthetic data generation, and domain adaptation to enhance model performance in data‑scarce scenarios.

Required Qualifications

• A minimum of a bachelor's degree in Nuclear Engineering, Computer Science, or a related field, with a strong focus on machine learning and artificial intelligence.
• A minimum of ten (10) years of relevant experience.
• Proven expertise in deep learning frameworks such as TensorFlow and PyTorch.
• Proficiency in programming and tools, including Python, C++, and MATLAB.
• Experience with nuclear codes such as MCNP, Serpent, OpenMC, and CTF.
• Demonstrated ability to develop and validate hybrid modeling techniques in nuclear engineering contexts.
• Familiarity with uncertainty quantification methods and transfer learning techniques.
• Strong publication record in reputable journals and conferences, showcasing contributions to AI and nuclear engineering.
• Excellent communication skills and ability to present complex technical concepts to diverse audiences.
• Must be a U.S. citizen.
• Must be able to obtain and maintain a U.S. Department of Energy (DOE) or Department of Defense (DOD) security clearance as required.

Preferred Qualifications

• Ph.D. or M.Sc. in Nuclear Engineering, Computer Science, or a related field.
• Experience integrating machine learning capabilities into established nuclear engineering workflows.
• Knowledge of optimization methods and their applications in reactor design and fuel cycle analysis.
• Familiarity with generative models, such as conditional variational autoencoders (CVAE), for synthetic data generation.

What We Offer

• Competitive salary and benefits package, including health, dental, and retirement plans.
• Flexible work schedules and paid time off to promote a healthy work‑life balance.
• Professional development opportunities, including mentorship programs and sponsorship for continuing education.
• An inclusive atmosphere that celebrates new perspectives and supports collaboration.
• The chance to be part of a mission‑driven organization making a positive impact on the future of energy.
• Opportunities for continuous learning and training to grow throughout your career.

Pay

Base salary range: $86,000.00 – $136,000.00 per year, with potential additional elements such as annual cash incentive and comprehensive benefits. Total compensation may vary based on market factors and candidate qualifications.

Equal Employment Opportunity

BWXT is committed to the concept of Equal Employment Opportunity. We have established procedures to ensure that all personnel actions such as recruitment, compensation, career development, benefits, company-sponsored training and social recreational programs are administered without regard to citizenship, age, protected veteran or other protected status.