Interested in this Data Scientist role at General Dynamics Information Technology?
Apply Now →Skills & Technologies
About This Role
Clearance Level Top Secret Category Data Science and Data Engineering Locations Fort Bragg, North Carolina
Joint Base Lewis-McChord, Washington
Fort Bragg, North Carolina
Coronado, California (Onsite Workplace)
Key Skills For Success Analytics
Data Analysis
Datasets
Quantitative Analysis
Research Methodologies
REQ#:RQ229806
Public Trust:None
Requisition Type:Regular
Your Impact
Own your opportunity to be on the frontlines of health innovation. Deliver for America’s health agency missions and enhance lives for hundreds of millions every day.
Job Description
We are GDIT. The people supporting and securing some of the most complex government, defense,
and intelligence projects across the country. We ensure today is safe and tomorrow is smarter. Our work has meaning and impact on the world around us, but also on us, and that’s important.
GDIT is your place. You make it your own by embracing autonomy, seizing opportunity, and being rusted to deliver your best every day.
We think. We act. We deliver. There is no challenge we can’t turn into opportunity. And our work depends on an Occupational Therapist joining our team to support customer activities.
The Data Science Advisor (DSA) leads the POTFF Enterprise Data and Analytics function and serves as the primary analytical advisor to the Enterprise POTFF Director, Domain Leads, and the Human Factors Council (HFC) on research methodologies, data collection strategies, and program evaluation. The DSA coordinates and directs the technical work of the Data Engineers, Data Scientists, and Senior Data Scientists under their purview, subject to Government direction; sets the analytical agenda in response to Government priorities; ensures that methods are sound, reproducible, and defensible; and delivers findings to Government leadership in a form suitable for decision-making.
HOW A DATA SCIENCE ADVISOR (DSA) WILL MAKE AN IMPACT:
- Serve as the lead for the Enterprise Data and Analytics function: plan, prioritize, assign, and review the work of the Data Engineers, Data Scientists, and Senior Data Scientists under their purview in response to Government-directed requirements, and manage the analytical backlog using an Agile or comparable framework.
- Serve as the primary analytical advisor to the Enterprise POTFF Director, Domain Leads, and the HFC regarding research methodologies, data collection strategies, study design, and program evaluation.
- Design and oversee Government-directed program evaluations, analyses, and experiments that measure the validity, efficacy, and Return on Investment (ROI) of POTFF interventions; select appropriate statistical models and study designs; and ensure data quality and defensible, reproducible methodology.
- Establish and maintain the team’s analytical standards, data governance practices, reporting methodologies, and performance-measurement frameworks in coordination with Government data governance, and ensure compliance with DoW Impact Level 5 (IL5), HIPAA, Privacy Act, and applicable DoW artificial intelligence and machine learning (AI/ML) policy.
- Translate Government priorities into analytical requirements; support the strategic planning process for POTFF programs with data-driven decision support; and support the development of enterprise performance measures, executive dashboards, and readiness metrics.
- Prepare and deliver reports, briefings, and decision-support products that accurately convey findings, limitations, and recommendations to Government leadership.
- Provide consultation to supported units and POTFF staff on opportunities and methods for capturing data on POTFF programs and initiatives, and on the interpretation of analytical products.
- Coordinate with the USSOCOM Enterprise Data Team and other Government data organizations on data access, standards, and shared platforms, as directed by the Government.
- Perform advanced analysis personally when required, and maintain currency in statistical analysis, data science, AI/ML, data engineering, and visualization technologies.
- Coordinate and advise on the training and professional development of the data science personnel under their purview, subject to Government approval.
WHAT YOU’LL NEED TO SUCCEED:
EDUCATION: Master’s degree in a quantitative discipline, including but not limited to statistics, biostatistics, mathematics, applied mathematics, data science, operations research, computer science, engineering, physics, economics or econometrics, epidemiology, geospatial science or geoinformatics, or psychology or another behavioral science with a quantitative-methods concentration, with documented coursework or professional application in statistics, programming, and quantitative analysis; or a bachelor’s degree in such a discipline with the additional experience stated under Required Minimum Experience/Skills below.
- With a Master’s degree: minimum of seven (7) years of professional experience applying data science, statistics, or research methods to real-world problems, including at least two (2) years supervising or leading analysts. With a bachelor’s degree: minimum of ten (10) years of such experience, including at least two (2) years supervising or leading analysts. In either path, at least three (3) years must be hands-on practice as a data scientist, statistician, or equivalent analyst personally performing the analyses, rather than supervising or advising. Degree-program work does not count toward the Data Science Advisor experience requirement.
- Demonstrated expertise in research methods, study design, and program evaluation, including quantitative and qualitative methods, sufficient to design defensible evaluations and to advise senior leaders on their limitations.
- Advanced proficiency in Python or R and in SQL, and demonstrated experience delivering statistical or predictive models and decision-support products to organizational leadership.
- Demonstrated supervisory experience leading a data science, analytics, or research team, and experience running a program or project of comparable scope.
- Ability to communicate effectively both orally and in writing with senior leadership, negotiate complex issues, develop a range of alternatives when there is no single solution, and establish and maintain good working relations with stakeholders.
PREFERRED QUALIFICATIONS:
- Desired (not required): Experience with human performance, sports science, health, clinical, or biomedical data or research.
- Desired (not required): Agile or Scrum certification (e.g., Certified ScrumMaster, PMI Agile Certified Practitioner) or comparable training.
- Desired (not required): A doctoral degree in one of the disciplines listed above, or a history of peer-reviewed publications.
- Desired (not required): Familiarity with the Government-furnished technology stack (e.g., Databricks and Apache Spark, Power BI, Microsoft Power Platform) and with Palantir Foundry.
LOCATION: Various CONUS SITES
CLEARANCE: Secret or Top-Secret Clearance, or the ability to obtain clearance.
This is a contingent posting, expected to start in 2027.
GDIT IS YOUR PLACE:
- 401K with company match
- Comprehensive health and wellness packages
- Internal mobility team dedicated to helping you own your career
- Professional growth opportunities including paid education and certifications
- Cutting-edge technology you can learn from
- Rest and recharge with paid vacation and holidayS
#MilitaryHealthGDITJobs
Work Requirements
Years of Experience 8 + years of related experience
- may vary based on technical training, certification(s), or degree
Certification Travel Required Less than 10%
Citizenship U.S. Citizenship Required
Salary and Benefit Information
The likely salary range for this position is $128,039 - $173,229. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.
Our Identity Verification Process
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.
About Our Work
We are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50+ countries worldwide, offering leading mission-ready capabilities in AI, cloud, cyber and software development.
Join our Talent Community to stay up to date on our career opportunities and events at gdit.com/tc.
Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans
Salary Context
This $128K-$173K range is below the median for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At General Dynamics Information Technology, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 21% below the category median. Disclosed range: $128K to $173K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Agent Developer, AI Software Engineer. Positions span Tampa, FL, US, Joint Base Lewis McChord, WA, US, Hurlburt Field, FL. Compensation range: $98K - $253K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.