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About This Role
What Impact You'll Have
Our team is charged with taking commercial and academic innovation high\-side, in domains of Artificial Intelligence / Machine Learning (AI / ML), Natural Language Processing (NLP). We also bring the best ideas, tools, and approaches in technology infrastructure (AWS, DevOps, etc.) to the IC. The tech stack used is extremely broad \- anything cutting edge in the commercial market, the open source community, or the academic research community is likely involved: and if something isn't being looked at yet, you can make that happen.
This effort supports ALL missions of the Intelligence Community, including cyber\-related data science missions. A seamless group of contractor and customer personnel work to create innovations that supply customer groups with the data sets, models, algorithms, software, and infrastructure they need to increase their mission success. Management is hands off, gives the team the freedom to explore new approaches, and markets the best ideas and results to all the other IC customers.
This project regularly needs various types of people \- Data Scientists, Data / ETL Engineers, Analytic Software Engineers, Full Stack Developers, UI/UX Developers, and AWS/DevOps experts. We're particularly interested in people with any of the following experience:
Work on this program takes place throughout the Reston/Herndon/Chantilly, VA area (we cannot support remote work) and requires a TS/SCI \+ Poly clearance (acceptable to this customer).
What You'll be Owning
GRVTY is seeking a Data Engineer to join one of our top projects in Chantilly, VA. This role, requires working with a team of developers, data scientists, SMEs, and cyber analysts to design, develop, build, and analyze data management systems. The data engineer will work with and analyze our client's challenges and provide solutions by designing and implementing batch and streaming data pipelines.
- Design, develop, and maintain Python\-based data processing pipelines and workflow orchestration solutions for large\-scale text ingestion, transformation, and enrichment.
- Develop and implement AI\-powered agentic workflows and LLM\-integrated applications to automate data triage, classification, analysis, and processing tasks.
- Build, enhance, and maintain reusable AI capabilities, prompt frameworks, and agent\-based services that support enterprise data analysis platforms.
- Integrate and operationalize Large Language Models (LLMs) to deliver retrieval, reasoning, summarization, information extraction, and decision\-support capabilities.
- Troubleshoot, optimize, and scale text\-processing pipelines to ensure data quality, system reliability, and efficient AI\-driven workflows.
- Design and develop APIs and backend services that connect AI models, data pipelines, and mission applications.
- Collaborate with cross\-functional teams including data scientists, software engineers, and product stakeholders to prototype, test, and deploy AI\-enabled solutions.
- Develop Python\-based automation tools and services supporting data engineering, workflow orchestration, model integration, and operational efficiencies.
- Support the deployment and maintenance of production\-scale AI and machine learning solutions in mission\-focused environments.
- Evaluate emerging AI technologies and recommend enhancements that improve analytical capabilities and operational outcomes.
What You Must Have
- Active TS/SCI with Polygraph Clearance
- Develop and perform ETL on large unstructured datasets.
- Experience with Python
- Experience with services including Apache Kafka, Apache Spark, and Prefect
- Experience containerizing applications using Docker and deployments on Kubernetes
- Building and maintaining CI/CD pipelines for data and platform services
- Familiarity with Linux\-based systems
- Solid understanding of DevOps principles (automation, monitoring, reliability)
Why Choose GRVTY
The toughest national security challenges demand vision and ingenuity, not just resources. We deliver mission and technical expertise to outpace our adversaries. We're purpose\-built to tackle the most entrenched, systemic national security issues around the world.
We partner with our customers to help them overcome challenges in every corner of technology and defense—including the ones still being explored. Our growing capabilities create complementary advantages, giving on\-the\-ground operations the edge they need to succeed. We muster everything we have to answer every challenge presented, every day of our lives.
At GRVTY, we believe that when our employees thrive, our company thrives. That's why we offer a comprehensive and competitive benefits package designed to support your well\-being, growth, and work\-life balance.
- Robust health plan including medical, dental, and vision
- Health Savings Account with company contribution
- Annual Paid Time Off and Paid Holidays
- Paid Parental Leave
- 401k with generous company match
- Training and Development Opportunities
- Award Programs
- Variety of Company Sponsored Events
EEO Statement
GRVTY, is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran and will not be discriminated against on the basis of disability.
Anyone requiring reasonable accommodations should email [email protected] or call 703\-544\-7930 with requested details. A member of the HR team will respond to your request within 2 business days.
Know Your Rights: Workplace Discrimination is Illegal (eeoc.gov)
Please review our current job openings and apply for the positions you believe may be a fit. If you are not an immediate fit, we will also keep your resume in our database for future opportunities.
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 GRVTY, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
GRVTY AI Hiring
GRVTY has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Chantilly, VA, US, Laurel, MD, US, McLean, VA, US. Compensation range: $175K - $175K.
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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