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About This Role
Your Impact
Own your opportunity to support our nation's defense. Make an impact by connecting and securing critical operations across the globe, keeping our country safe and secure.
Job Description
DATA SCIENTIST SENIOR
YOUR IMPACT
Own your opportunity to work with the largest government agency in the nation. Make an impact by advancing the Department of War’s mission to keep our country safe and secure.
OUR COMPANY
Iron EagleX (IEX), a wholly owned subsidiary of General Dynamics Information Technology (GDIT), delivers agile IT and Intelligence solutions. Combining small\-team flexibility with global scale, IEX leverages emerging technologies to provide innovative, user\-focused solutions that empower organizations and end users to operate smarter, faster, and more securely in dynamic environments.
JOB DESCRIPTION
The Data Scientist will support the Data Stewardship/Tech Talent Program at Fort Bragg, NC.
MEANINGFUL WORK AND PERSONAL IMPACT
As a Data Scientist you will be responsible for designing, implementing, and maintaining scalable data pipelines while analyzing complex datasets to drive meaningful insights. You will plan, execute, and manage end‑to‑end machine learning projects using cloud‑native platforms and advanced ML tools. Your work will draw on a broad range of methodologies—such as data mining, natural language processing, and machine learning—to solve challenging problems and enable data‑driven decision‑making.
As a Data Scientist you will also be responsible for applying strong programming, mathematical modeling, and statistical expertise to build applied models and communicate results clearly to stakeholders. You will work with large structured and unstructured datasets using distributed SQL, relational and non‑relational data technologies, and languages such as Python and R. Your ability to translate analytical findings into actionable organizational value will be essential to success in this role.
JOB DUTIES (INCLUDE BUT ARE NOT LIMITED TO)
- Interpret and analyze data using exploratory statistical and mathematical techniques.
- Clean, prepare, and organize structured and unstructured data using programming tools.
- Conduct experiments on data and generate insights to solve complex data challenges.
- Partner with Data Engineers to build and support scalable data environments.
- Apply scientific methods, statistics, and algorithms to define problems and test hypotheses.
- Build predictive and prescriptive models, NLP solutions, clustering, forecasting, and other advanced analytics.
- Design and automate processes for large‑scale data manipulation and integration.
- Process and analyze large datasets using frameworks such as Spark and Hadoop.
- Conduct statistical modeling and develop visualizations using tools like Power BI, Tableau, or R Shiny.
- Design and implement algorithms using languages such as Python and R.
- Perform data engineering and modeling on cloud platforms such as Databricks, Snowflake, IBM Cloud Pak, or Cloudera.
- Communicate complex analytical results to non‑technical audiences through clear data storytelling.
REQUIRED SKILLS:
- Proficient with one or more programming languages (Java, C\+\+, Python, R, etc.).
- Proficient in Agile Development and Git Operations.
- Demonstrated experience applying data science methods to real\-world data problems.
WHAT YOU’LL NEED TO SUCCEED
- Clearance: Current DoW SECRET clearance.
- Experience: 5 years of related experience.
- Education: Bachelor’s degree in a STEM field.
- Certifications: N/A
- Role requirements: On\-site with hybrid potential pending customer approval.
- Due to US Government Contract Requirements, only US Citizens are eligible for this role
GDIT IS YOUR PLACE
At GDIT, the mission is our purpose, and our people are at the center of everything we do.
- Growth: AI\-powered career tool that identifies career steps and learning opportunities
- Support: An internal mobility team focused on helping you achieve your career goals
- Rewards: Comprehensive benefits and wellness packages, 401K with company match, competitive pay and paid time off
- Community: Award\-winning culture of innovation and a military\-friendly workplace
OWN YOUR OPPORTUNITY
Explore a career at GDIT and you’ll find endless opportunities to grow alongside colleagues who share your passion for the mission and delivering results. \#iexjobs \#iexpriority
Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans
\#iexjobs
Work Requirements
Years of Experience
1 \+ years of related experience* may vary based on technical training, certification(s), *or* degree
Certification
Travel Required
None
Citizenship
U.S. Citizenship Required
Salary and Benefit Information
The likely salary range for this position is $106,250 \- $143,750\. 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.
View information about benefits and our total rewards program.
About Our Work
We are GDIT. A global technology and professional services company that delivers technology 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 over 50 countries worldwide, offering leading capabilities in digital modernization, AI/ML, cloud, cyber and application development. Together with our customers, we strive to create a safer, smarter world by harnessing the power of deep expertise and advanced technology.
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 $106K-$143K 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 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($125K) sits 37% below the category median. Disclosed range: $106K to $143K.
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.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span IN, US, Fort Bragg, NC, US, Tampa, FL, US. Compensation range: $103K - $207K.
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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