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About This Role
About DMI:
DMI is a leading provider of digital services and technology solutions, headquartered in Tysons Corner, VA. With a focus on end-to-end managed IT services, including managed mobility, cloud, cybersecurity, network operations, and application development, DMI supports public sector agencies and commercial enterprises around the globe. Recognized as a Top Workplace, DMI is committed to delivering secure, efficient, and cost-effective solutions that drive measurable results. Learn more at www.dminc.com About the Opportunity:
DMI, LLC is seeking an experienced Data Scientist / Machine Learning Specialist to support data-driven decision-making, advanced analytics, statistical modeling, and machine learning initiatives within a complex enterprise environment. The ideal candidate will have strong experience working with large and complex datasets and will be capable of developing analytical models and translating data into actionable insights.
The successful candidate will apply advanced statistical, analytical, and machine learning techniques to solve complex business and mission challenges. This individual will work closely with technical teams, business stakeholders, and organizational leadership to identify analytical opportunities, develop data-driven solutions, and communicate findings effectively to both technical and non-technical audiences.
The ideal candidate will also have experience with business intelligence platforms, data management practices, and cross-organizational collaboration, including efforts requiring consensus building and system authorization. Duties and Responsibilities:* Design, develop, and implement advanced data science, statistical modeling, and machine learning solutions to address complex business and mission requirements.
- Analyze large, complex datasets to identify trends, patterns, relationships, and actionable insights.
- Develop and validate statistical and machine learning models using appropriate analytical techniques and methodologies.
- Perform data exploration, preparation, transformation, feature engineering, and statistical analysis to support modeling and decision-making.
- Develop analytical solutions using Python, R, SQL, and other appropriate data science technologies.
- Build dashboards, visualizations, and analytical products using business intelligence tools such as Power BI and Tableau.
- Translate complex analytical findings into clear, actionable recommendations for business leaders, mission stakeholders, and other decision-makers.
- Present analytical results and technical concepts to both technical and non-technical audiences.
- Collaborate with data engineers, software developers, system owners, business stakeholders, and organizational leadership to develop and implement data-driven solutions.
- Apply data management best practices to support data quality, integrity, security, privacy, and appropriate use of organizational data.
- Contribute to the development and implementation of data governance and analytical standards.
- Identify opportunities to improve existing analytical processes, models, reporting capabilities, and data products.
- Support the documentation of analytical methodologies, models, assumptions, results, and technical processes.
- Collaborate across organizational boundaries to build consensus, resolve issues, and align stakeholders on data-driven solutions.
- Support activities associated with system authorization, compliance, and operational readiness as required.
- Stay current with emerging data science, machine learning, statistical modeling, visualization, and analytics technologies and methodologies.
Qualifications: Education and Years of Experience:* Bachelor's degree or higher in statistics, computer science, mathematics, or a related field.
- Minimum of 3-5 years of relevant work experience in data science, statistical modeling, and machine learning.
Required Skills & Certifications:* Strong analytical and quantitative skills, including expertise in Python, R, and SQL.
- Demonstrated ability to design and implement complex analytical models and techniques to analyze large, complex data sets.
- Experience with business intelligence tools such as Power BI or Tableau.
- Knowledge of data management best practices, including data quality, security, and privacy regulations.
- Excellent communication and presentation skills, including the ability to communicate complex technical concepts to non-technical audiences.
- Experience collaborating across organizations, building consensus, and obtaining required system authorizations.
Clearance Requirements: Must possess or be eligible to obtain and complete a government security screening and/or a Secret security clearance. Citizenship Status Required: Must be a U.S. Citizen Physical Requirements: None required for this position. Location: Remote, US Minimum Salary Rate: $102,495 Median Salary Rate: $128,119 Working at DMI:
DMI is a diverse, prosperous, and rewarding place to work. Our culture is shaped by five core values that guide how we work, grow, and succeed together:* Do What’s Right – We lead with honesty and integrity.
- Own the Outcome – We take responsibility and deliver.
- Deliver for Our Customers – We are relentless about delivering value.
- Think Bold, Act Smart – We innovate with purpose.
- Win Together – We collaborate and celebrate our success.
These values aren’t just ideals—they show up in how we support every part of your well-being:* Convenience/Concierge – Virtual health visits, commuter perks, pet insurance, and entertainment discounts that make life easier.
- Development – Annual performance reviews, tuition assistance, and internal career growth opportunities to help you thrive.
- Financial – Generous 401(k) matches, life and disability insurance, and financial wellness tools to support your future.
- Recognition – Annual awards, service anniversaries, referral bonuses, and peer-to-peer shoutouts that spotlight your achievements.
- Wellness – Healthcare coverage, wellness programs, flu shots, and biometric screenings to support your health.
DMI values employees for their talents and contributions, and we take pride in helping our customers achieve their goals. Because when we live our values, we all win together. * No Agencies Please *
Applicants selected may be subject to a government security investigation and must meet eligibility requirements for access to classified information. US citizenship may be required for some positions.
Salary Context
This $102K-$128K range is in the lower quartile for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At DMI, 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 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($115K) sits 39% below the category median. Disclosed range: $102K to $128K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
DMI AI Hiring
DMI has 2 open AI roles right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $128K - $128K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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