Head of Data at Zap Energy

Everett, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

25 Jun, 26

Salary

210000.0

Posted On

27 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Infrastructure, AI Infrastructure, Data Pipelines, Machine Learning, AI Strategy, Data Lake Architecture, Data Governance, Data Engineering, Scientific Analysis, Team Leadership, Python, ETL Workflows, Data Quality, Scoping, Prototyping, HPC Simulations

Industry

Energy Technology

Description

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 a technical strategy to non-technical stakeholders. Comfortable working as a player-coach: leading through others while staying technically engaged. Preferred Experience with data lake architecture and modern data stack tooling (e.g., dbt, Spark, cloud storage platforms). Experience managing multidimensional datasets such as time series, images, and video. Experience in a fast-paced R&D or deep-tech environment. Some knowledge of plasma physics and fusion science Education Bachelor's degree in computer science, engineering, physics, or a related technical field. PhD in physics or related field strongly preferred. Why Zap Energy Fusion is one of the hardest and most important problems humanity is working on — and Zap Energy has a genuinely differentiated approach. You’ll work alongside world-class physicists and engineers, with access to real experimental data from a functioning fusion machine. The data and AI work here is not a nice-to-have; it is core to how we learn, iterate, and win. Your work will matter. Estimated salary range: $170,000-210,000 annually. Candidates may be considered for other positions at Zap Energy and actual salary will be based on relevant education, experience, and other qualifications. Range is published in accordance with Washington Equal Pay and Opportunity Act. Zap Energy Inc. is an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws and regulations. Questions on this position can be sent to careers@zapenergyinc.com.

Responsibilities

The Head of Data will own and evolve the data and AI infrastructure for the R&D organization, managing experimental data pipelines, operational dashboards, and the strategic vision for AI usage across the company. This role involves leading a team using data science and machine learning to accelerate scientific analysis related to plasma physics and fusion science.