Interested in this Data Scientist role at HunaTek Government Solutions?
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About This Role
Position Summary:The DoW Data Scientist – War Data Platform is a mission‑focused technical role responsible for developing advanced data science, analytics, and data‑engineering solutions within the Department of Defense War Data Platform ecosystem. The role requires deep expertise integrating and analyzing large‑scale operational, logistical, readiness, and force‑design data using modern tools such as Foundry and Advana. The Data Scientist supports warfighter‑focused decision‑making by building predictive models, optimizing pipelines, and delivering high‑quality analytics products that directly inform strategy, resource allocation, and senior‑leader decisions. This position is hybrid with on‑site engagement depending on mission needs. Essential Job Functions:
- War Data Platform / Mission Analytics + Develop data science solutions directly within the War Data Platform, enabling large‑scale analysis of operational, logistics, personnel, and readiness datasets. + Design workflows and analytics that fuse multi‑domain data to support warfighter decisions. + Implement repeatable modeling, automation, and analytic pipelines optimized for secure DoD environments.
- Foundry / Advana Development + Build and maintain high‑quality data pipelines in Foundry, including transforms, ontology development, and code‑workbook integrations. + Develop dashboards, visual analytics, and decision‑support tools within Advana, leveraging PySpark, SQL, and platform‑native frameworks. + Ensure all analytics comply with DoD governance, security, and data‑management standards.
- Data Science & Modeling + Perform exploratory data analysis and create predictive, prescriptive, and statistical models to support mission outcomes. + Build machine learning models to address forecasting, optimization, readiness assessments, and operational planning. + Visualize complex analytical findings in accessible, decision‑focused formats.
- Data Engineering & Integration + Ingest, clean, fuse, and normalize large structured and unstructured datasets from multiple DoD data sources. + Develop scalable ETL/ELT pipelines using Python, SQL, PySpark, and platform‑specific tools. + Deploy automated workflows and CI/CD patterns for stable, repeatable analytics delivery.
- Collaboration, Communication & Advisory Support + Work closely with program managers, mission owners, analysts, engineers, and operators to identify analytical requirements. + Serve as a technical advisor to senior leaders on War Data Platform capabilities and analytic potential. + Translate complex data science outputs into clear, operationally meaningful insights.
Skills and Qualification Required:
- U.S. Citizenship required.
- Clearance eligibility required.
- Bachelor’s degree and 5–10+ years in data science, machine learning, data engineering, or advanced analytics.
- Direct experience developing analytic products in Foundry and/or Advana.
- Experience supporting DoD programs, warfighter missions, or secure data platforms.
- Deep hands‑on experience with the DoD War Data Platform, including its architecture and operational workflows.
- Strong working knowledge of Foundry and Advana, including transforms, pipelines, ontologies, and workbook‑based analytics.
- Proficiency in Python, SQL, PySpark, and major data‑science libraries.
- Experience working in secure cloud environments with strict compliance and governance controls.
- Ability to build advanced ML/statistical models and communicate insights clearly.
- Ability to collaborate in fast‑paced, mission‑centric teams.
- Proficiency in developing interactive dashboards, data models, and visual analytics using Power BI.
- Experience building Qlik Sense applications, data transformations, and visual storytelling dashboards.
- Ability to integrate Power BI and Qlik solutions with DoD datasets and secure cloud platforms.
Skills and Qualifications Preferred:
- Master’s degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or related field.
- Experience with operational, logistics, readiness, wargaming, or campaign‑modeling datasets.
- Familiarity with DevSecOps, containerized workloads, Jupyter, Spark clusters, or data‑orchestration frameworks.
About Us:
At HunaTek, we build teams of people from all backgrounds with varying levels of experience, knowing firsthand that diversity of thought will strengthen our ability to deliver for our customers. We work hand in hand with Federal civilian and military staff, pulling together to further the interests of our nation and home and abroad. Whenever possible, we provide opportunities for our employees to learn new skills, obtain certifications, attend industry events, and have some fun together.
Our Benefits:
We offer a comprehensive benefits package designed to make sure our employees and their families have access to good health care, are insured against catastrophic health events, can put money aside for retirement and are able to maintain a healthy work-life balance. These benefits include:
- Comprehensive medical, dental and vision
- Long-term and short-term disability insurance and term life insurance
- 401(K) with safe harbor contribution
- Paid time off and 11 paid holidays
- Tuition and career development assistance
- A selection of voluntary benefits
This job description will be reviewed periodically as duties and responsibilities change with business necessity. Essential and marginal job functions are subject to modification. Equal opportunity employer as to all protected groups, including protected veterans and individuals with disabilities.
Salary Context
This $160K-$185K range is above the median for Data Scientist roles in our dataset (median: $162K across 195 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 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At HunaTek Government Solutions, 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 1,564 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($172K) sits 9% below the category median. Disclosed range: $160K to $185K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
HunaTek Government Solutions AI Hiring
HunaTek Government Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Quantico, VA, US. Compensation range: $185K - $195K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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