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About This Role
Overview:
At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.
Our Science \& Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus.
The National Security Directorate (NSD) drives science\-based, mission\-focused solutions to take on complex, real\-world threats to our nation and the world.
The Emerging Threats and Technologies Division, part of the National Security Directorate, consists of over 400 scientists, engineers, and analysts with backgrounds in cyber, nuclear, intelligence, policy, data science, and other fields. We work in interdisciplinary project teams to provide innovative concepts that integrate policy, analytics, science, and technology into unique solutions.
Rockstar Rewards:
Employees and their families are offered medical insurance, dental insurance, vision insurance, robust telehealth care options, several mental health benefits, free wellness coaching, health savings account, flexible spending accounts, basic life insurance, disability insurance\*, employee assistance program, business travel insurance, tuition assistance, relocation, backup childcare, legal benefits, supplemental parental bonding leave, surrogacy and adoption assistance, and fertility support. Employees are automatically enrolled in our company\-funded pension plan\* and may enroll in our 401 (k) savings plan with company match\*. Employees may accrue up to 120 vacation hours per year and may receive ten paid holidays per year.
- Research Associates excluded.
\*\*All benefits are dependent upon eligibility.
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Responsibilities:
ETT delivers threat\-informed approaches to research and development (R\&D) and a culture of research to operations to harness the opportunities from emerging and disruptive technologies while at the same time countering the threats that arise from them. Our teams address complex national security problems by applying cutting\-edge data science and leverage PNNL’s broad research capabilities to generate insight on emerging threats and technologies.
We are seeking a Data Scientist with expertise in AI and a passion for solving mission\-critical challenges to join our team. They will be a key member of an interdisciplinary team, focused on delivering data\-driven solutions that address critical national security challenges within the nuclear nonproliferation, policy, and emerging technology domains. This individual will collaborate with peers to apply AI and machine learning models, analyze datasets, work with domain experts for applied use alignment, and perform research that bridges cutting\-edge methods and field\-ready solutions. The candidate will support PNNL's mission by contributing to impactful R\&D projects that help tackle national challenges.
Successful candidates will have the opportunity to grow professionally while working on diverse, mission\-focused projects. At PNNL, we foster a collaborative and innovative work environment aimed at lifelong learning, creative problem\-solving, and advancing interdisciplinary, data\-driven innovation.
Responsibilities Include
- Design and implement AI/data science solutions to address complex national security problems, with a focus on nuclear nonproliferation, strategic policy analysis, and emerging/disruptive technology assessment.
- Design and implement AI/data science solutions to address complex technical challenges, including all\-source analysis, object detection, data fusion, and end\-to\-end data science workflows.
- Design, develop, and implement methods, processes, and systems to analyze diverse datasets, including approaches that traverse and integrate modalities.
- Demonstrate ability to transfer skills across application domains with a deployment\-focused approach, specifically in support of non\-proliferation mission.
- Develop and evaluate predictive models and advanced algorithms to extract actionable value from structured and unstructured data.
- Identify and assess domain\-relevant data resources; translate findings into actionable technical approaches and mission\-relevant capabilities.
- Apply machine learning, data modeling, and software engineering to integrate and clean data, quantify uncertainty, identify patterns, and generate insights from high\-dimensional datasets.
- Understanding how to traverse the nexus of policy, data science, and software engineering to develop data science solutions against real\-world constraints.
- Deliver solutions driven by exploratory data analysis and rapid prototyping, with a focus on transition to operational use.
- Build and maintain external partnerships that strengthen the team’s technical reputation and align outcomes with sponsor priorities.
- Serve as a principal investigator (PI) or co\-PI on projects and tasks that integrate multiple capabilities and/or interdisciplinary approaches.
- Mentor and guide junior staff, fostering technical excellence and professional development.
Qualifications:
Minimum Qualifications:* BS/BA and 5\+ years of relevant work experience \-OR\-
- MS/MA and 3\+ years of relevant work experience \-OR\-
- PhD with 1\+ year of relevant experience
Preferred Qualifications:* Demonstrated ability to rapidly assess ambiguous situations, exercise sound judgment with limited time/inputs, and communicate well\-considered recommendations.
- Experience applying traditional statistical methods, ML, and AI to unimodal and multimodal open\-source data (e.g., text, image, audio, video, graph), including preprocessing, representation/encoding, and task\-specific modeling/decoding.
- Experience with scientific data modalities include microscopy, radiation spectra, or mass spectrometry.
- Strong proficiency in Python and experience with ML/AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face).
- Experience navigating research codebases (e.g., notebooks, prototype scripts) and translating them into maintainable, production\-ready components.
- Experience designing and deploying scalable ML pipelines or AI\-enabled tools in operational or mission\-critical environments (on\-prem and/or cloud).
- Experience in data management for AI applications including system design, version control, and experimental dataset curation.
- Excellent communication and cross\-functional collaboration skills; ability to bridge research, engineering, and domain stakeholders.
- Proficiency in at least one general\-purpose programming language (e.g., Python, R, Julia, Java, C/C\+\+).
Experience working with sensitive and/or classified data in a national security context (preferred).
- Demonstrated experience applying Intelligence Community Directives pertaining to collection and disseminating all\-source intelligence analysis.
- Experience leading small to mid\-sized teams (approximately 5\+ staff) to deliver applied technical work.
- Experience briefing non\-technical audiences.
Hazardous Working Conditions/Environment:
Not Applicable
Additional Information:
This position requires the ability to obtain and maintain a federal security clearance.
A security clearance background investigation includes review of your employment, education, financial, and criminal history, as well as interviews with you and your personal references, neighbors, and co\-workers to determine trustworthiness, reliability, and loyalty to the United States. The investigation also examines your foreign connections, drug and alcohol use, foreign influence, and overall conduct.
Requirements:
- U.S. Citizenship
- Background Investigation: Applicants selected will be subject to a Federal background investigation and must meet eligibility requirements for access to classified matter in accordance with 10 CFR 710, Appendix B.
- Drug Testing: All Security Clearance positions are Testing Designated Positions, which means that the applicant selected for hire is subject to pre\-employment drug testing, and post\-employment random drug testing. In addition, applicants must be able to demonstrate non\-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the requisite Questionnaire for National Security Positions (QNSP).
Note: Applicants will be considered ineligible for security clearance processing by the U.S. Department of Energy if non\-use of illegal drugs, including marijuana, for 12 months cannot be demonstrated.
Testing Designated Position (TDP):
This position is a Testing Designated Position (TDP). The candidate selected for this position will be subject to pre\-employment and random drug testing for illegal drugs, including marijuana, consistent with the Controlled Substances Act and the PNNL Workplace Substance Abuse Program.
About PNNL:
Pacific Northwest National Laboratory (PNNL) is a world\-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. Every year, scores of dynamic, driven people come to PNNL to work with renowned researchers on meaningful science, innovations and outcomes for the U.S. Department of Energy and other sponsors; here is your chance to be one of them!
At PNNL, you will find an exciting research environment and excellent benefits including health insurance, and flexible work schedules. PNNL is located in eastern Washington State—the dry side of Washington known for its stellar outdoor recreation and affordable cost of living. The Lab’s campus is only a 45\-minute flight (or \~3 hour drive) from Seattle or Portland, and is serviced by the convenient PSC airport, connected to 8 major hubs.
Commitment to Excellence and Equal Employment Opportunity:
Our laboratory is committed to fostering a work environment where all individuals are treated with fairness and respect while solving critical challenges in fundamental sciences, national security, and energy resiliency. We are an Equal Employment Opportunity employer.
Pacific Northwest National Laboratory (PNNL) is an Equal Opportunity Employer. PNNL considers all applicants for employment without regard to race, religion, color, sex, national origin, age, disability, genetic information (including family medical history), protected veteran status, and any other status or characteristic protected by federal, state, and/or local laws.
We are committed to providing reasonable accommodations for individuals with disabilities and disabled veterans in our job application procedures and in employment. If you need assistance or an accommodation due to a disability, contact us at [email protected].
Drug Free Workplace:
PNNL is committed to a drug\-free workplace supported by Workplace Substance Abuse Program (WSAP) and complies with federal laws prohibiting the possession and use of illegal drugs.
If you are offered employment at PNNL, you must pass a drug test prior to commencing employment. PNNL complies with federal law regarding illegal drug use. Under federal law, marijuana remains an illegal drug. If you test positive for any illegal controlled substance, including marijuana, your offer of employment will be withdrawn.
HSPD\-12 PIV Credential Requirement:
As a national laboratory, PNNL is responsible for adhering to the Homeland Security Presidential Directive 12 (HSPD\-12\) and Department of Energy (DOE) Order 473\.1A, which require new employees to obtain and maintain a HSPD\-12 Personal Identify Verification (PIV) Credential. To obtain this credential, new employees must successfully complete the applicable tier of federal background investigation post hire and receive a favorable federal adjudication. The tier of federal background investigation will be determined by job duties and national security or public trust responsibilities associated with the job. All tiers of investigation include a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last 1 to 7 years (depending on the applicable tier of investigation). Illegal drug activities include marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.
For foreign national candidates:
If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) Federal risk determination to maintain employment. Once you meet the three\-year residency requirement thereafter, you will be required to obtain a PIV credential to maintain employment. The tier of federal background investigation required to obtain the PIV credential will be determined by job duties at the time you become eligible for the PIV credential.
Salary Context
This $140K-$228K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Pacific Northwest National Laboratory, 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 $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $140K to $228K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Pacific Northwest National Laboratory AI Hiring
Pacific Northwest National Laboratory has 1 open AI role right now. They're hiring across Data Scientist. Based in Richland, WA, US. Compensation range: $228K - $228K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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