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About This Role
DCCA is a veteran\-owned high\-technology company specializing in providing information technology services to a variety of government agencies and commercial enterprises since 1982\. DCCA is proud to offer a strong, competitive compensation and benefits package. Visit our website at: www.dcca.com .
Data Scientist
CANDIDATES MUST HAVE AN ACTIVE TS/SCI w/ Poly
For over 40 years, DCCA has provided a broad range of IT services to government agencies and commercial enterprises, helping them to feel confident in their IT infrastructure. With DCCA, these organizations can be confident in the flexibility and skill of their IT partners, allowing them to upgrade their technology quickly and efficiently. Better yet, thanks to DCCA’s successful track record, clients can rest assured knowing DCCA can tackle any problem with ease, allowing them to focus on the work that matters.
Internally, DCCA prides itself on a culture built on integrity and inclusivity, allowing its employees to build lasting skills and relationships. As a veteran owned business, DCCA knows the importance of recruiting employees with a wide range of backgrounds, allowing for every problem to be approached by a diverse array of perspectives. Join us and be part of a team that has a people first mentality and a dedication to excellence.
Key Tasks:
- Advanced Analytics \& Modeling: Design, develop, and validate predictive models, statistical algorithms, and machine learning models to solve complex federal business and operational challenges.
- Data Processing \& Pipeline Integration: Extract, clean, and analyze large volumes of structured and unstructured data from disparate sources using ETL tools and cloud\-native data services (e.g., AWS Glue, Apache Kafka).
- Anomaly Detection \& Fraud Prevention: Implement advanced algorithms (such as Isolation Forests) to detect anomalies in large transactional or operational datasets, enhancing audit efficacy and risk mitigation.
- Data Visualization \& Reporting: Translate complex analytical findings into intuitive dashboards, reports, and interactive visualizations using tools like Amazon QuickSight, Tableau, or Power BI to empower executive decision\-making.
- Cross\-Functional Collaboration: Collaborate with cross\-functional Agile teams, subject matter experts, and AI/ML engineers to seamlessly integrate data science models into production applications and microservices.
- Security \& Data Governance: Ensure all data handling and model deployments strictly adhere to federal data privacy regulations, HIPAA, the Privacy Act, and DoD/NIST security standards.
- Continuous Improvement: Monitor model performance in production environments, retraining and tuning models as necessary to prevent drift and ensure sustained accuracy over time.
Required Skills:
- Experience: Minimum of 5\+ years of professional experience in data science, advanced analytics, or machine learning engineering.
- Programming Languages: High proficiency in Python and R for data manipulation and modeling, as well as strong advanced SQL skills for complex database querying (e.g., PostgreSQL, Oracle).
- Machine Learning Frameworks: Extensive hands\-on experience with core ML and data science libraries (e.g., Pandas, NumPy, Scikit\-Learn, TensorFlow, PyTorch).
- Cloud \& Big Data: Demonstrated experience operating within cloud ecosystems (AWS GovCloud preferred) and utilizing big data technologies and data lakes (e.g., AWS S3, EMR, Redshift).
- Clearance: TS/SCI w/Poly.
Desired Skills :
- Prior experience supporting federal government agencies (e.g., Intelligence Community) with complex data analytics.
- Familiarity with Natural Language Processing (NLP) and Edge AI applications.
- Experience with DevSecOps practices, CI/CD pipelines, and version control (e.g., Git) for collaborative model development (MLOps).
- Active industry certifications (e.g., AWS Certified Data Analytics, AWS Certified Machine Learning, or equivalent).
Education/Certifications:
- Education: Bachelor’s or master’s degree in data science, Statistics, Applied Mathematics, Computer Science, or related quantitative discipline.
The proposed salary range for this position in Virginia is 195,000 to 260,000\. Final salary will be determined based on various factors. Our comprehensive benefit offerings include healthcare, retirement plan, paid disability and life insurance programs, employee assistance program, paid and unpaid leave programs, education assistance, and wellness initiatives.
At DCCA, we believe the key to providing our clients with unrivaled services starts with retaining top talent, something we’re able to do through our consistent commitment to building culture and comprehensive benefits.
Competitive Compensation: While salary at DCCA is determined by various factors, we are committed to making sure our salaries reflect the skill and expertise of our employees. In addition, each year we perform an annual salary review ensuring pay is equitable across both the company and industry at large.
Growth Opportunities: DCCA makes it a priority to help you grow and support your career advancement. From upskilling programs to recertification support, to professional development opportunities, we’re here to help you grow your career and create lasting relationships.
Emphasis on Inclusivity: DCCA’s culture emphasizes respect, equity, and opportunity and is supported by an array of business resource groups and other opportunities for connection.
Empowering Health: DCCA’s benefits which encompass healthcare, paid time off, and flexible 401(k) options encourage you to live a healthy and fulfilling life, both in and outside of work. Learn more about our total benefits package on our Benefits page.
Mission Focused Work: From the defense industry to health IT management, DCCA allows you to work on innovative projects whose outcomes improve people's lives and solve today’s IT problems.
Equal Opportunity Employer including Disability/Vets
Salary Context
This $195K-$260K 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 DCCA, 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 ($227K) sits 18% above the category median. Disclosed range: $195K to $260K.
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.
DCCA AI Hiring
DCCA has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Chantilly, VA, US. Compensation range: $260K - $260K.
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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