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About This Role
Location Chantilly, Virginia Category Engineering, Technology, & Science Job Type Full time Job Id R2128453 Posted Date 10/09/2026
Title:
Data Scientist
At KBR, we deliver science, technology and engineering solutions to governments and companies around the world. KBR employs approximately 37,000 people worldwide with customers in more than 85 countries and operations in over 28 countries. KBR is proud to work with its customers across the globe to provide technology, value-added services, and long-term operations and maintenance services to ensure consistent delivery with predictable results.
Trinzic is the future independent company to be created through the planned spin-off of KBR's Mission Technology Solutions business, expected in January 2027. Our people work at the frontier of what is possible, delivering solutions in challenging environments and advancing some of the world's highest-priority national security and space missions.
Backed by decades of mission success, deep expertise, and trusted partnerships, Trinzic will launch as a global organization with more than 18,000 employees supporting customers around the world. Join us at a defining moment and help shape the future of a company built to tackle complex challenges and make a meaningful impact.
Candidates who join KBR's Mission Technology Solutions business before the spin-off will transition to Trinzic when the separation is complete and will not need to reapply for employment.
Belong. Connect. Grow. with KBR!
KBR’s National Security Solutions team provides high-end engineering and advanced technology solutions to our customers in the intelligence and national security communities. In this position, your work will have a profound impact on the country’s most critical role – protecting our national security.
Why Join Us?
- Innovative Projects: KBR’s work is at the forefront of engineering, logistics, operations, science, program management, mission IT and cybersecurity solutions.
- Collaborative Environment: Be part of a dynamic team that thrives on collaboration and innovation, fostering a supportive and intellectually stimulating workplace.
- Impactful Work: Your contributions will be pivotal in designing and optimizing defense systems that ensure national security and shape the future of space defense.
Who You Are:
KBR is looking for a motivated Data Scientist to join our team and provide Systems Engineer Technical Advisor (SETA) support for the Intelligence Community Customer Ground Enterprise Directorate (GED). GED plays a critical role in enabling the Intelligence Community Customer to plan and execute intelligence-gathering missions, transforming collected data into useful information for the Intelligence Community (IC) and Department of Defense (DoD) users.
In this role, you will have the chance to design, develop, test, and deliver products that support our customers’ advanced systems. In support of GED Front Office, Chief Architect, Integrated Intelligence Program Office, Advanced Ground Office, and Enterprise Data Applications and Platforms. You'll collaborate with multi-disciplinary teams focused on hardware, software, system integration, and data analysis to help achieve important missions and meet requirements.
This position is contingent upon contract award.
Primary Responsibilities:
- Conducts scientific Research and Development towards the tasking, collection, processing, and dissemination of data.
- Collects substantial amounts of data using analytical, statistical, and programmable skills.
- Analyze data sets to discover actionable patterns and trends for business use.
- Uses scientific methods, processes, algorithms, and systems to extract insights from structured and unstructured data.
- Applies mathematical, problem-solving, and coding skills to manage big data, extracting valuable insights.
- Assess other’s R&D proposals for feasibility and progress towards mission goals.
- Solve difficult, non-routine problems by applying advanced analytical methodologies and improving analytic methodologies.
- Developing custom searches.
- Communicating and coordinating with internal and external partners as needed.
- Other duties as assigned.
Required Qualifications:
- Active TS/SCI with Polygraph.
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
- Minimum of 10 years of experience in data science or related roles.
- Expert-level proficiency in Python or R.
- Strong experience with machine learning frameworks (scikit-learn, TensorFlow, PyTorch).
- Deep understanding of statistical modeling, experimental design, and advanced analytics.
- Proficiency in SQL and working with large-scale datasets.
- Experience building and deploying production-ready models.
- Strong software engineering practices (version control, testing, modular coding).
- Excellent communication and storytelling skills with data.
Desired Qualifications:
- Experience with big data technologies (Spark, Hadoop).
- Experience working with cloud platforms (AWS, Azure, or Google Cloud).
- Knowledge of MLOps tools and practices (CI/CD, model monitoring, containerization).
- Experience with deep learning, NLP, recommender systems, or time-series forecasting.
Ready to Make a Difference?
If you’re excited about making a significant impact in the field of space defense and working on projects that matter, we encourage you to apply and join our team at KBR. Let's shape the future together
Basic Compensation:
$161,200 –221,600 annum (This range is for the VA area only)
The rate offered will be based on the selected candidate’s work location, knowledge, skills, abilities and/or experience, contract affordability and consideration of internal parity.
Additional Compensation:
KBR may offer bonuses, commissions, or other forms of compensation to certain job titles or levels, per internal policy or contractual designation. Additional compensation may be in the form of sign on bonus, relocation benefits, short-term incentives, long-term incentives, or discretionary payments for exceptional performance.
KBR Benefits
KBR offers a selection of competitive lifestyle benefits which could include 401K plan with company match, medical, dental, vision, life insurance, AD&D, flexible spending account, disability, paid time off, or flexible work schedule. We support career advancement through professional training and development
Belong, Connect and Grow at KBR
At KBR, we are passionate about our people and our Zero Harm culture. These inform all that we do and are at the heart of our commitment to, and ongoing journey toward being a People First company. That commitment is central to our team of team’s philosophy and fosters an environment where everyone can Belong, Connect and Grow. We Deliver – Together.
KBR is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity or expression, age, national origin, veteran status, genetic information, union status and/or beliefs, or any other characteristic protected by federal, state, or local law.
Salary Context
This $161K-$221K range is above the 75th percentile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Trinzic, 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 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $161K to $221K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Trinzic AI Hiring
Trinzic has 4 open AI roles right now. They're hiring across Research Scientist, Data Scientist, AI/ML Engineer. Positions span Beavercreek, OH, US, Chantilly, VA, US, Lexington Park, MD, US. Compensation range: $157K - $221K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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