Sr. Scientist, Computational Materials & Data Science

About Niron

Niron Magnetics is commercializing the first new magnetic material in decades powered by its breakthrough material formulation and advanced manufacturing process. The company’s proprietary magnet technology based on Iron Nitride enables magnets that are inherently high magnetization, free of rare earths and other critical materials, and solve supply chain reliability challenges, will drive innovation in various industries. Headquartered in Minneapolis, MN, Niron Magnetics is comprised of a team of professionals with a desire to make a positive impact on the global community.

We were named one of “America's Top Green Tech Companies” for 2024 and 2025 by TIME Magazine and the “Innovation of the Year” at the 2025 mHUB Fourth Revolution Awards.

Our team is made up of people who think big, dare to innovate, and strive to impact the planet through technological innovation for our customers. Ready to work alongside amazing people, solve complex problems, and leave a legacy? Join our team.

The Sr. Scientist, Computational Materials & Data Science will report to the Director of R&D and play a critical role in Niron’s Next Gen R&D effort. This role is tightly aligned to Niron’s prioritized R&D projects, which span the magnet process end-to-end - from powder formation and surface engineering through compaction and final magnetic and mechanical performance.

The successful candidate will combine physics based simulation (e.g., multi physics, particle, and microstructure modeling) with data science methods (statistical modeling, machine learning, design of experiments, and robust data workflows) to accelerate learning, guide experimental priorities, and derisk scaleup in support of Next Gen magnet performance (MGOe) and scaleup readiness milestones. This role is highly collaborative, working closely with experimental scientists, engineers, project leaders, and technical leadership.

• Develop physics based and data driven models that support Niron’s strategic R&D projects, with emphasis on magnet performance and process robustness.
• Perform multi-physics modeling of magnetic particle and powder behavior under magnetic, thermal, and mechanical fields, supporting projects such as phase creation & stabilization, powder processing, and powder compaction.
• Apply nanoscale and mesoscale modeling to understand powder handling, compaction, and microstructure evolution during processing.
• Build and maintain analysis ready datasets by structuring, cleaning, and joining experimental, process, and characterization data; document assumptions and data context so results are reproducible and reusable across project teams.
• Use statistical modeling and machine learning to identify key drivers, quantify sensitivities, and provide decision recommendations across the five projects.
• Apply design of experiments (DOE) and learning loop approaches to reduce experimental cycle time and accelerate convergence on target technical milestones.
• Integrate simulation outputs with experimental data to inform project decisions, prioritize experiments, and accelerate progress toward magnet performance and scaleup readiness deliverables.
• Develop clear visualizations, technical readouts, and decision tools to communicate model assumptions, results, uncertainty, and recommended actions to cross functional teams and technical leadership.
• Collaborate within cross functional project teams including regular interaction with the Director of R&D, CTO, and project managers.

This position is a key enabler of Niron’s Next Gen R&D strategy, where the majority of R&D effort is focused on a portfolio of projects tied directly to company milestones. The Computational Materials Scientist will help build an integrated simulation + data science learning loop that drives technical decisions, accelerates magnet performance gains, and supports pilots cale readiness.

• Bachelor’s degree in materials science, physics, magnetic materials, computational science, or a related field.
• Minimum of 5 years of experience in industrial or applied R&D.
• Demonstrated expertise in multiphysics modeling…