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About This Role
Overview:
Data Architect / Data Scientist (Senior) Arlington, VA
Are you ready to enhance your skills and build your career in a rapidly evolving business climate? Are you looking for a career where professional development is embedded in your employer’s core culture? If so, Chenega Military, Intelligence \& Operations Support (MIOS) could be the place for you! Join our team of professionals who support large\-scale government operations by leveraging cutting\-edge technology and take your career to the next level!
The Senior Data Architect / Data Scientist designs, develops, and implements advanced data architectures and analytics solutions supporting CDAO data\-driven mission capabilities. Directly supports CDAO stakeholders while ensuring quality deliverables.
Responsibilities:
- Design scalable enterprise data architectures supporting large\-scale analytics and AI initiatives.
- Develop data pipelines and analytics models supporting mission decision\-making.
- Build and deploy machine learning models supporting operational analytics.
- Support development of data governance and data management frameworks.
- Integrate multiple data sources across enterprise environments.
- Implement big data technologies supporting advanced analytics platforms.
- Collaborate with engineers, analysts, and mission stakeholders to build consensus and deliver data solutions.
- Other duties as assigned.
Qualifications:
- Bachelor’s degree in Data Science, Computer Science, Mathematics, Engineering, or related field.
- 8\+ years of experience in data engineering, data science, or analytics.
+ Experience supporting federal and/or DoD customers.
+ Experience with machine learning frameworks, data analytics tools, and cloud\-based data platforms.
- Active Top Secret security clearance required.
Preferred Qualifications:* Master’s degree preferred.
- Experience supporting DoD, CDAO, Joint Staff, or Combatant Command data, AI, and digital modernization initiatives.
- Demonstrated experience designing and implementing cloud\-based data architectures, data lakes, and advanced analytics platforms within AWS, Azure, or similar environments.
- Experience developing and deploying machine learning, artificial intelligence, and predictive analytics solutions supporting mission operations and decision\-making.
- Professional certifications such as AWS Certified Data Analytics, Azure Data Engineer Associate, Databricks Certified Data Engineer, or equivalent.
Knowledge, Skills, and Abilities:* Ability to work on\-site 5 days a week
- Ability to obtain SCI Clearance
- Ability to work independently and yet be effective within a team setting
- Must be capable of managing multiple efforts with time\-related constraints in a fast\-paced contracting environment
- Demonstrated ability to effectively communicate and collaborate with diverse internal and external stakeholder groups and individuals
- Friendly presence, helpful attitude, good interpersonal skills, and ability to work well with others.
- Excellent skills in Microsoft Word, Excel, and other Office applications
- Proficient with Microsoft Office Applications, and experience working in a home office setting, as well as the ability to train end users on frequently asked technical issues.
- Ability to provide technical assistance and support over the phone; good phone skills, professional demeanor, and previous customer service experience strongly desired.
- Good problem\-solving skills; ability to visualize a problem/situation and think abstractly to solve it
How you’ll grow
At Chenega MIOS, our professional development plan focuses on helping our team members at every level of their careers to identify and use their strengths to do their best work every day. From entry\-level employees to senior leaders, we believe there’s always room to learn.
We offer opportunities to help sharpen skills in addition to hands\-on experience in the global, fast\-changing business world. From on\-the\-job learning experiences to formal development programs, our professionals have a variety of opportunities to continue to grow throughout their careers. Benefits
At Chenega MIOS, we know that great people make a great organization. We value our team members and offer them a broad range of benefits.
Learn more about what working at Chenega MIOS can mean for you. Chenega MIOS’s culture
Our positive and supportive culture encourages our team members to do their best work every day. We celebrate individuals by recognizing their uniqueness and offering them the flexibility to make daily choices that can help them be healthy, centered, confident, and aware. We offer well\-being programs and continuously look for new ways to maintain a culture where we excel and lead healthy, happy lives. Corporate citizenship
Chenega MIOS is led by a purpose to make an impact that matters. This purpose defines who we are and extends to relationships with our clients, our team members, and our communities. We believe that business has the power to inspire and transform. We focus on education, giving, skill\-based volunteerism, and leadership to help drive positive social impact in our communities.
Learn more about Chenega’s impact on the world.
Chenega MIOS News\- https://chenegamios.com/news/ Tips from your Talent Acquisition Team
We want job seekers exploring opportunities at Chenega MIOS to feel prepared and confident. To help you with your research, we suggest you review the following links:
Chenega MIOS web site \- www.chenegamios.com
Glassdoor \- https://www.glassdoor.com/Overview/Working\-at\-Chenega\-MIOS\-EI\_IE369514\.11,23\.htm
LinkedIn \- https://www.linkedin.com/company/1472684/
Facebook \- https://www.facebook.com/chenegamios/
\#Chenega Agile Real Time Solutions, LLC
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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 Chenega Corporation, 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.
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.
Chenega Corporation AI Hiring
Chenega Corporation has 5 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Research Scientist. Positions span Arlington, VA, US, Springfield, VA, US, Fort Detrick, MD, US. Compensation range: $90K - $90K.
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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