Interested in this Data Scientist role at Helion Energy?
Apply Now →Skills & Technologies
About This Role
About Helion
We are a fusion power company based in Everett, WA, with the mission to build the world's first fusion power plant, enabling a future with unlimited clean electricity. Our vision is a world with clean, reliable, and affordable energy for everyone.
Since Helion's founding in 2013, we have raised $1.5 billion from long-time investors such as Sam Altman, Mithril, and Capricorn Investment Group as well as new investors including Thrive Capital, LightSpeed Venture Capital, SoftBank, and others to propel us forward. Our current prototype, Polaris, has reached record-breaking plasma temperatures of 150 million degrees Celsius (13 keV). Helion is continuing to iterate and on the path to the world's first fusion power plant, Orion.
This is a pivotal time to join Helion. You will tackle real-world challenges with a team that prizes urgency, rigor, ownership, and a commitment to delivering hard truths – values essential to achieving what no one has before. Together, we will change the future of energy, because the world can't wait.
What You Will Be Doing:
You will be responsible for developing the data and knowledge foundation that enables engineers to understand, operate, and improve Helion’s fusion systems. In this role, you will partner closely with engineers, scientists, and operators to transform complex experimental and operational data (including machine telemetry, experimental results, and engineering workflows) into structured datasets, knowledge systems, and AI-enabled tools that support analysis and decision-making. You will combine data science, knowledge modeling, and applied AI techniques to make fragmented, rapidly evolving information accessible and actionable at scale. This is an onsite role that reports directly to our R&D Manager at our Everett, WA office.
You Will:
- Analyze real-time and historical system data to provide control room operators with actionable insights, improve operational decision-making, and accelerate root-cause and performance analysis
- Develop data schemas, ontologies, and metadata frameworks to organize and standardize complex technical information
- Prototype decision-support tools for anomaly detection, experiment comparison, and operational reporting
- Define metrics and evaluation frameworks to ensure effectiveness, reliability, and business impact of statistical models and AI solutions
- Collaborate with the enterprise software engineering team to build and evaluate AI-powered workflows, including retrieval, summarization, and anomaly analysis systems using operational data
- Partner across Software Engineering and Data Science teams to evolve and maintain data pipelines that transform and integrate diverse data sources into analytics-ready datasets
Required Skills:
- 7+ years of experience applying statistics, data science, and quantitative methods to complex hardware, physics, or engineering data
- Experience analyzing operational, experimental, and time-series data to identify trends, diagnose hardware behavior, uncover anomalies, and inform engineering decisions is preferred
- Ability to translate ambiguous problems into practical, scalable solutions and partner effectively with engineers and scientists
- Proficiency in Python and modern data science tooling, with experience building data pipelines, analytics infrastructure, or production data systems
- Experience developing structured representations of information, including data models, schemas, metadata frameworks, or ontologies
- Familiarity with LLM APIs and applied AI techniques such as RAG, embeddings, semantic search, and AI-powered workflow automation
Benefits
Our total compensation package includes benefits, including but not limited to:
- Medical, Dental, and Vision plans for employees and their families
- 31 Days of PTO (21 vacation days and 10 sick days)
- 10 Paid holidays, plus company-wide winter break
- Up to 5% employer 401(k) match
- Short term disability, long term disability, and life insurance
- Paid parental leave and support (up to 16 weeks)
- Annual wellness stipend
Helion is an equal opportunity employer and value diversity at our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need assistance or an accommodation during the interview process, please let us know.
Compensation Range: $195K - $222K
Salary Context
This $195K-$222K range is above the 75th percentile for Data Scientist roles in our dataset (median: $167K across 150 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Helion Energy, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($208K) sits 10% above the category median. Disclosed range: $195K to $222K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Helion Energy AI Hiring
Helion Energy has 1 open AI role right now. They're hiring across Data Scientist. Based in Everett, WA, US. Compensation range: $222K - $222K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.