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About This Role
Lead Data Scientist/Data Analyst
HunaTek is seeking a highly motivated, performance\-driven individual with strong data analysis experience to work closely with our Government client in the Northern VA area. Successful candidates will have excellent communication, quantitative, research, and analytical skills and the capability to lead, or principally contribute to, several of the largest and most significant studies in HunaTek’s portfolio. Experience in financial management, logistics, engineering, and/or program management domains are also preferred. As part of our team, you’ll help our Government clients make well informed, data\-driven decisions on some of the most impactful challenges.
Essential Job Functions:
A career with HunaTek is both rewarding and challenging. Our clients require skilled, hardworking, innovative professionals who are able to meet the demands of a dynamic and fast\-paced workplace. The analyst position will include, but is not limited to, the following capabilities:
- Architect, design, code, and implement next\-generation data analytics using best practices in the latest technologies.
- Develop solutions to enable metadata/rules engine\-driven data analytics application leveraging open source and/or cloud native components.
- Develop solutions in a highly collaborative and agile environment.
- Lead efforts in collecting, normalizing, and analyzing complex data.
- Work directly with stakeholders to understand their needs and requirements and apply these to the build/test phase of reports and dashboards.
- Identify opportunities to streamline reporting to make processes more automated and less manual.
- Develop and manage business intelligence solutions for the organization.
- Utilize data visualization tools like Power BI to deliver solutions sourcing a variety of data.
- Build data visualizations including dashboards and reports.
- Document data profiling and quality assurance results, data dictionaries and analytical findings.
- Use data to create predictive models and trend analysis.
- Collaborate with colleagues for the purpose of collecting and structuring data.
- Collect, audit, compile, and validate data from multiple sources.
- Interpret data, analyze results using statistical techniques or relational databases and produce ongoing as\-needed reports to meet client and/or business needs.
- Communicate and work with diverse organizations and representatives for data collection, analysis, and results presentation.
- Provide thought leadership clearly demonstrating an understanding of the customer mission and innovative ideas to address business challenges of the customer.
- Work autonomously and as part of a team to meet organizational and project goals.
- Lead in the preparation of briefing materials and other communications packages presenting the results of analysis.
- Support on\-site delivery in the Quantico and Stafford areas.
Skills and Qualifications Required:
- US Citizenship is required.
- Active Secret Security Clearance or ability to obtain a Secret Security Clearance.
- Proficiency in Microsoft Excel and experience working with large data sets.
- Advanced Microsoft Power BI skills, to include demonstrated development of models and data architectures using DAX and Power Query editor.
- Excellent data gathering and analytical skills.
- Excellent oral and written communication skills.
- Have a strong understanding of DoD systems and processes.
- Ability to lead detail\-oriented tasks and multi\-task under minimal supervision.
Education and Experience:
- Bachelor’s degree in Mathematics, Statistics, Computer Science, Physical Science, Engineering or related field
- 5\+ years of experience supporting solutions implemented in DoD.
- Experience in statistical modeling.
- Experience with Power BI, Power Query, Power Automate, Power Apps, MS Excel, Python, and SQL.
- Experience using Python or R packages and libraries (e.g., scikit\-learn, pandas, plotly, shiny, etc.) to develop predictive analyses and machine learning models.
- Preferred: Experience with cloud computing/storage, data lakes/warehouses.
- Preferred: Experience with Spark and ML applications.
- Preferred: Experience with Scala, Java
- Preferred: Experience with Robotic Process Automation (RPA) (e.g., UiPath)
- Preferred: Experience with Natural Language Processing (NLP)
- Preferred: Experience in Databricks
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 $140K-$170K range is below the median 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 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($155K) sits 20% below the category median. Disclosed range: $140K to $170K.
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.
HunaTek Government Solutions AI Hiring
HunaTek Government Solutions has 1 open AI role right now. They're hiring across Data Scientist. Based in Arlington, VA, US. Compensation range: $170K - $170K.
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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