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About This Role
Title: Data Scientist I
Location:
Newport News, Virginia, US, 23606
Category: Computer Science Technology and IT
Work Arrangement Type: Hybrid \- About 60% on\-site
Classification: Staff Computer Scientist I
Description:
At Jefferson Lab, you’ll champion cutting\-edge science and operational excellence while shaping the future of discovery. Join us and make your mark – where excellence meets purpose, and great minds truly matter.
Jefferson Lab is hiring 2 Data Scientists for 2 \-year term appointments.
Range: $80,300 \- $142,300 (SCS\-I)
What your job will be like:
Work on all elements of the data science workflow (data preprocessing, analyzing data using exploratory mathematics, statistical techniques, developing and optimizing AI/ML models, developing agentic workflows). Recommend various technology options or approaches for system and processes improvements in terms of performance, efficiency, cost or safety.
In this job you will:
Participate in the research and development in machine learning and data science focused on applications such as:
AI/ML based active control of large complex systems.
Surrogate modeling of particle accelerator processes, deployment and continual learning workflows.
Uncertainty Quantification for ML.
Develop and maintain high quality software for data science projects (machine learning, continual learning, uncertainty quantification).
Experience
Required: None
Preferred: 2 or more years of Postdoc experience
Preferred: Experience with contributing and leading tasks on large projects with multi\-disciplinary teams
Education
Required: Bachelor's Degree Computer Science, Data Science, Applied Mathematics, Computer Engineering, or a closely related technical area
Preferred: Ph.D.
Knowledge, Skills, and Abilities
Proficiency in Python and familiarity with publicly available technical libraries for data analytics (e.g. scikit\-learn), deep learning (e.g. Pytorch, Tensorflow) and optimization tools
Proficiency in advance machine learning such as uncertainty quantification for ML, continual learning, graph neural networks,
And/or proficiency in agentic workflow tools such as LangChain, LangGraph,
And/or proficiency in Large Language Models training and evaluation
Ability to work with large datasets and mine relevant information for use in AI/ML applications
Ability to develop approaches and solutions to complex problems in the forms of proposals, software, documents or other work products
About Jefferson Lab
Join a community with a common purpose of solving the most challenging scientific and engineering problems of our time. The Jefferson Lab campus is located in southeastern Virginia amidst a vibrant and growing technology community.
A career at Jefferson Lab is more than a job. You will be part of “big science” and work alongside top scientists and engineers from around the world unlocking the secrets of our visible universe. Managed by SURATech, LLC, Thomas Jefferson National Accelerator Facility is entering an exciting period of mission growth and is seeking new team members ready to apply their skills and passion to have an impact. You could call it work, or you could call it a mission. We call it a challenge. We do things that will change the world.
Total Rewards at Jefferson Lab
At Jefferson Lab, we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Our benefits program includes, but is not limited to:
- Medical, Dental, and Vision Care Plans • Flexible Spending Accounts
- Paid Time\-off and Leave Programs (Paid Parental, vacation, holidays, and sick leave)
- 401(k) Plan – 9% Lab Contribution; 100% vested • Flexible Work Arrangements
(Remote \& Alternate Work Schedules available)
- Tuition Assistance, Training and Professional Development Programs
- Live near the waterways of the Chesapeake Bay region with access to nearby beaches,
mountains, and all major metropolitan centers on the East Coast
SURATech, LLC manages and operates the Thomas Jefferson National Accelerator Facility (Jefferson Lab). SURATech is an Equal Opportunity Employer.
SURATech is committed to providing reasonable accommodation for people with disabilities (unless doing so will result in an undue hardship). If you need a reasonable accommodation for any part of the employment process, please send an e\-mail to [email protected] or contact Human Resources by calling (757\) 269\-7100 and selecting option 1 between 8 am – 5 pm EST to provide the nature of your request.
Employment with SURATech is conditional upon DOE approval if at any time during your employment you are participating in a Foreign Government Talent Recruitment Program or Affiliated activity. Generally, such programs/activities include any foreign\-state\-sponsored attempt to acquire U.S.\-funded scientific research through programs run or funded by the government that target scientists, engineers, students, academics, researchers, and entrepreneurs of all nationalities working or educated in the United States. This includes positions or appointments, both domestic and foreign, titled academic, professional, or institutional appointments whether or not remuneration is received and whether full\-time, part\-time or voluntary.
Nearest Major Market: Hampton Roads
Salary Context
This $80K-$142K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Jefferson Lab, 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($111K) sits 44% below the category median. Disclosed range: $80K to $142K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Jefferson Lab AI Hiring
Jefferson Lab has 1 open AI role right now. They're hiring across Data Scientist. Based in Newport News, VA, US. Compensation range: $142K - $142K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
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