Interested in this Data Scientist role at General Dynamics Information Technology?
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About This Role
Clearance Level
Top Secret SCI \+ Polygraph
Category
Data Science and Data Engineering
Location
McLean, Virginia
*(Onsite Workplace)*
Key Skills For Success
Data Engineering
Data Science
Oracle
Structured Query Language (SQL)
##### REQ\#:RQ224779
##### Public Trust:None
##### Requisition Type:Regular
##### Your Impact
Own your opportunity to serve as a critical component of our nation’s safety and security. Make an impact by using your expertise to protect our country from threats.
Job Description
-------------------
The Data Scientist will deploy, fine\-tune, and monitor production machine learning models in a production environment. Additionally, they will provide support in the areas of data extraction, transformation and load (ETL), data mapping, analytics, operations, databases, and maintenance of data and associated systems. As a member of the team, candidate will work in a multi\-tasking, quick\-paced, dynamic, process\-improvement environment that requires experience with the principles of data science, data modeling, data mapping, data testing, data quality, and documentation preparation. This is a mission focused role requiring experience with deploying models in a production environment against real\-time collection.
HOW A DATA SCIENTIST WILL MAKE AN IMPACT
- Create and maintain custody of production machine learning models across a variety of tasks, including but not limited to audio extraction, object recognition, Natural Language Processing (NLP), and other generic classification tasks
- Optimize existing machine learning services to better utilize current GPU capabilities and assist with road mapping future GPU requirements
- Deploy machine learning models against streaming data, designed to provide near\-real time analytics to augment decision making
- Improve data architecture decisions with data engineers to better stage data for continuous training models in production
- Provide support in the areas of data extraction, transformation and load (ETL), data mapping, analytics, operations, databases, and maintenance of data and associated systems
REQUIRED TECHNICAL SKILLS
- Demonstrated experience with the following: Python, Cuda, Kubernetes, CI/CD. Apache Kafka, REST architecture, Open\-AI, LLMs, NLP, YOLO/Object Recognition, Whisper/Audio processing
- Demonstrated experience translating data insights into tools or analytic capabilities that inform operational decisions and/or improve processes
- Demonstrated experience with relational databases (SQL, Oracle) and NoSQL databases (Elasticsearch, Neo4J, Redis)
- Demonstrated experience with GPU processing
- Demonstrated experience applying machine learning methodologies to build high\-quality prediction models
- Familiar with servers operating systems; Windows, Linux, Distributed Computing, Blade Centers, and cloud infrastructure
- Familiar with database methodologies
- Familiar with Source code management and integration (ex \- GitHub/GitLab, Jenkins, RunDeck)
- Familiar with Data Science frameworks such as Keras, Tensorflow, or Theano
- Ability to work well in a fast\-paced, constantly evolving work environment with a focus on continual process improvement and a proactive approach to problem solving
WHAT YOU’LL NEED TO SUCCEED:
- 10\+ years of related data science/statistical experience and 2\+ years of software engineering or data engineering experience
- Bachelor’s or Technology degree in Engineering or a related specialized area/field, OR equivalent 4 additional years job\-related experience
- TS/SCI with Polygraph clearance
- Excellent organizational, coordination, interpersonal and team building skills
- Location: At Customer Site – near Tyson Corner
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
\#OpportunityOwned
\#GDITCareers
\#WeAreGDIT
\#GDITPolyEvent
\#JET
\#GDITEnhanced2025
### Work Requirements
Years of Experience
10 \+ 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 $212,500 \- $287,500\. 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 $212K-$287K range is above the 75th percentile 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 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 $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($250K) sits 30% above the category median. Disclosed range: $212K to $287K.
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.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 13 open AI roles right now. They're hiring across Data Scientist, Data Engineer, AI/ML Engineer, AI Software Engineer. Positions span Remote, US, Arlington, VA, US, Chantilly, VA, US. Compensation range: $154K - $287K.
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.
Frequently Asked Questions
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