Head of Data

About Zap Energy

Zap Energy is building a low-cost, compact, and scalable fusion energy platform that confines and compresses plasma without the need for expensive and complex magnetic coils. Our sheared-flow-stabilized Z-pinch technology offers one of the shortest potential paths to commercially viable fusion and requires orders of magnitude less capital than traditional approaches. With over 130 employees across two facilities near Seattle, and backed by leading financial and strategic investors, Zap Energy is at an exciting inflection point — scaling our machine and our data ambitions together.

The Opportunity

As Head of Data, you will own and evolve the data and AI infrastructure that the entire R&D organization depends on — from the experimental and simulation data pipelines and operational dashboards that drive day-to-day machine operations, to the strategic vision for how AI will be used across Zap Energy. Additionally, you will manage team efforts which employs data scientific methods, machine learning techniques and AI to accelerate scientific analysis and learning on the core plasma physics foundations and fusion science that the sheared-flow-stabilized Z-pinch concept rests upon.

This is a player-coach role. You will lead a small, high-impact team while staying hands-on — particularly in scoping, prototyping, and driving AI initiatives, driving data lake architecture and automating advanced analytical techniques. You will be the internal expert and advocate for how Zap harnesses emerging information technologies to broadly accelerate our science and engineering efforts. If you are energized by rigorous science, enjoy building things that matter, and want to shape the data and AI culture of a fusion company at a pivotal moment, this role is for you.

Responsibilities

Data Infrastructure & Pipelines

• Lead the ongoing hardening and reliability improvement of our core data pipelines, which serve as the backbone for R&D operations and our machine diagnostics dashboard.
• Oversee Zap's data lake initiative, ensuring alignment on architecture, data governance, and integration with existing pipelines for experimental data, diagnostics, and simulations.
• Lead the data engineering team to improve robustness, observability, and maintainability of data systems.

AI Strategy & Implementation

• Inform and execute Zap’s AI roadmap — identifying high-value opportunities across the company, prioritizing initiatives, and shepherding projects from concept to production.
• Be hands-on in building early AI projects: scoping problems, generating ideas, writing code, and demonstrating proof of concepts that others can build on.
• Coordinate AI usage across Zap Energy, partnering with physics, engineering, and operations teams to apply AI/ML where it creates the most leverage.
• Build and maintain relationships with external AI partners and collaborators, representing Zap’s technical interests in joint work.

Physics Analysis Workflows

• Employ ML/AI algorithms to speed up scientific analysis from experimental measurements as well as HPC simulations
• Maintaining and improving specific data analysis protocols

Team Leadership

• Lead and grow a team of 4 (currently including data engineers and physics data analysts), setting direction, priorities, and a culture of rigor and curiosity.
• Collaborate closely with the VP of R&D and R&D stakeholders to align data and AI priorities with company goals.
• Act as the internal champion for data quality, best practices, and responsible AI usage.

Qualifications

Required

• 8 + years of experience in data science, ML engineering, or related fields, with scientific or engineering teams in fast paced research and development environments
• At least 2 years in a team lead or management role.
• Demonstrated experience deploying machine learning models and AI systems in production or research environments.
• Experience leading or participating in scientific investigations in academic or industrial environments
• Hands-on Python experience with scientific/data libraries (NumPy, Pandas, scikit-learn, or equivalent).
• Experience with data pipeline design, ETL workflows, and data quality practices.
• Proven ability to develop and communicate