Spring 2027 Data Science Intern

The Nuclear Company is the fastest growing AI tech-startup in the nuclear and energy space, pioneering a fleet-scale approach to building the next generation of nuclear reactors. Through our design-once, build-many model, we're accelerating the deployment of safe, reliable, and affordable nuclear energy.

We operate with an AI-first mindset. Every employee is expected to leverage AI, technology, and the Nuclear Operating System (NOS) as integral components of their role to improve the quality, speed, and impact of their work. We expect every team member to continuously identify opportunities to automate workflows, enhance decision-making, improve processes, and contribute to the ongoing evolution of NOS as a strategic operating capability that enables The Nuclear Company to scale with excellence.

We hire people who are driven by purpose, thrive in ambiguity, and are energized by building what has never been built before. Our team combines intellectual curiosity with high agency, embraces candid feedback and continuous learning, and holds themselves and others to exceptional standards. Our values—
Trust, Responsibility, Unity, Scrappiness, and Tenacity
—guide how we hire, collaborate, and make decisions every day. They are not words on a wall; they are the standard by which we operate. Trust is the foundation of our safety culture, fostering intellectual honesty, accountability, and open communication, while our values challenge every team member to execute with urgency, humility, resilience, and an unwavering commitment to our mission.

The United States is building nuclear power again, and The Nuclear Company is writing the software that makes it possible. NOS, the Nuclear Operating System, runs the regulatory, supply chain, cost, schedule, and field work behind real reactor construction. The work matters for national security, energy independence, and the climate.

You will turn data into decisions that move a nuclear build forward. As a Data Science Intern, you do the full applied data science job on NOS: frame the question, wrangle the data, build and validate models, and put the answer in front of the people who act on it. You will work on forecasting and decision analysis for mega project cost and schedule, extend the NOS ontology and data model, and build the analyses and dashboards leaders use to make real calls.

You work next to full-time data scientists and ship work that reaches production.

This is a six-month co-op available in Spring 2027 (January to June), aligned to the academic calendar. Base location is Washington DC, on-site five days a week, with full housing and relocation for co-ops outside the DC metro area.

• Frame business and engineering questions with stakeholders, then source, clean, and explore the data needed to answer them.
• Build and validate models (forecasting, classification, anomaly detection, decision analysis) for mega project cost, schedule, and risk.
• Engineer features and extend the NOS ontology and data model across regulatory, supply chain, cost, and schedule domains.
• Build data pipelines across Palantir Foundry, AWS Gov Cloud, and more.
• Design metrics, dashboards, and analyses that operators, engineers, and executives use to make decisions, and communicate findings clearly.

• Currently enrolled in a BS or MS in Data Science, Statistics, Computer Science, Applied Math, or a related quantitative field. Available for a full six-month term and returning to school afterward.
• Strong Python and the data stack (pandas, Num Py, scikit-learn), with working knowledge of SQL.
• Foundation in statistics and probability, and a track record of completed data analysis (course, personal, research, or prior internships).
• This position requires access to information and technology subject to U.S. export controls (including DOE 10 CFR Part 810 and NRC requirements). U.S. Person status (U.S. citizen or lawful permanent resident) is required, and TNC does not provide visa sponsorship for these roles.
• Willing and able to work on-site in Washington DC, five days a week, for the full six-month term.

• Data visualization and…