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About This Role
Senior Data Scientist \- Databricks Genie \& AI Analytics \#1087
Clearance: U.S. Citizenship is required.Current CBP/DHS clearance highly preferred.
High\-Level Project Summary: Dev Technology is seeking a highly skilled Data Scientist with hands\-on experience in the Databricks Data Intelligence Platform, including Databricks Genie, to help transform how business and technical users interact with enterprise data. This role will develop AI\-powered analytics solutions that enable users to query data using natural language, generate insights from complex datasets, and accelerate decision\-making across the organization. The ideal candidate combines strong data science and machine learning expertise with advanced knowledge of Databricks, data engineering concepts, semantic modeling, and generative AI applications.
What You'll Be Doing: You will be part of a mission\-focused delivery team that encompasses application development and analytics work, and helping to drive innovation to solve agency mission concerns.
- Design, develop, and deploy advanced analytics and machine learning solutions using Databricks
- Configure and optimize Databricks Genie spaces to enable natural language access to enterprise data
- Develop semantic models, business metadata, and governance processes that improve the accuracy of Genie\-generated responses
- Develop and maintain notebooks, workflows, dashboards, and data pipelines within Databricks
- Collaborate with data engineers to ensure high\-quality, governed datasets are available for Genie and AI applications
- Support development of retrieval\-augmented generation (RAG), LLM, and AI\-agent use cases leveraging Databricks capabilities
Required Education, Experience, and Skills:
- Bachelor's degree and ten years of working experience in the information technology field
- 5\+ years of experience in data science, machine learning, advanced analytics, or AI/ML engineering
- 2\+ years of hands\-on experience with Databricks
- Experience configuring and administering Databricks Genie
- Strong expertise in Python, SQL, PySpark
- Experience developing semantic layers, data models, and business\-friendly metadata structures
- Experience working with structured and unstructured datasets
- Strong understanding of data governance, data quality, and metadata management
- Experience with cloud platforms such as Azure, AWS, or GCP
- Ability to communicate complex analytical concepts to non\-technical stakeholders
Preferred Education, Experience, and Skills:
- Databricks certification
- Experience supporting federal agencies
Who We Are
Dev Technology is a growing IT company with an employee\-centric culture that works on mission\-critical projects for the federal government. We partner with our federal customers to deliver technology services and solutions, and to drive our client's missions forward through innovation. We use Agile and DevSecOps principles to provide services including application development, biometrics and identity management, cloud and infrastructure optimization, IT and legacy modernization, and data management.
As a Washington Post Top Workplace award winner for the past THIRTEEN years in a row, the Top Workplaces USA for the past five years, and a recipient of the Companies As Responsive Employers (CARE) Award for the past six years, Dev Technology employees enjoy:
- Generous and flexible time\-off policy
- Flexible work schedules and telework options, including remote work availability for eligible projects
- Career development opportunities including a mentorship program, technical and management training through Dev University, hands\-on learning through DevLab, tuition reimbursement, and paid training opportunities
- Industry\-leading benefits including a choice of two health plans that include dental and vision, flexible spending account, commuter benefits, life insurance, and more
- 401K matching with a 5% matching contribution
- Regular team and company social events including our annual party, happy hours, fitness challenges, and more
- A focus on community engagement including company wide support activities, employer match for donations, and time off for volunteer efforts
- *To learn more about working at Dev Technology, visit* *Working At Dev Technology Group*
*Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans*
Dev Technology Group operates in the following states: AL, AR, AZ, CO, DC, FL, GA, ID, IL, IN, MD, MA, ME, MI, MN, MO, MS, NC, NJ, OH, OR, PA, SC, TN, TX, VA, WV.
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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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Dev Technology Group, 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.
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.
Dev Technology Group AI Hiring
Dev Technology Group has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Reston, VA, US, Ashburn, VA, US. Compensation range: $160K - $160K.
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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