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About This Role
Description: POSITION: Mid Data Scientist
WORK LOCATION: Charlottesville, VA
JOB CATEGORY: Intelligence
JOB TYPE: Full\-Time
REQUISITION ID: EM43\-021
CITIZENSHIP: United States Citizen
CLEARANCE TYPE: TS/SCI W/CI Poly
TRAVEL REQUIREMENTS: Up to 10%
E\&M Technologies, Inc. is dedicated to recruiting and developing diverse, high\-performing talent who are passionate about what they do. Our employees are unified in a shared dedication to our customers’ mission and quest for professional growth. E\&M provides an inclusive, engaging environment designed to empower employees and promote work\-life success. Fundamental to our culture is an unwavering focus on values, dedication to our communities, and commitment to excellence in everything we do.
E\&M Technologies, Inc. is currently seeking a Mid Data Scientist to join our team in Charlottesville, VA in support of the Defense Resources and Infrastructure Office (DRI). DRI's mission is to catalog and analyze military and civilian infrastructure and defense\-related resources that underpin a nation's or group's military capabilities\-including transportation, logistics, defense economics, energy systems, arms trade, physical vulnerability to weapon effects, defense industries, and the operational environment\-to support U.S. and allied planners, targeteers, warfighters, policymakers, partners, and acquisition official.
Job Responsibilities:
- Conducts data analytics, data engineering, data mining, exploratory analysis, predicative analysis, and statistical analysis, and uses scientific techniques to correlate data into graphical, written, visual and verbal narrative products, enabling more informed analytic decisions. Proactively retrieves information from various sources, analyzes it for better understanding about the data set, and builds AI tools that automate certain processes.
- This role encompasses both Data Science and Data Engineering disciplines. Requires raw coding experience without AI assisted IDEs.
- Creating various ML\-based tools or processes such as recommendation engines or automated lead scoring systems.
- Performs statistical analysis, applies data mining techniques, and builds high\-quality prediction systems.
- Should be skilled in data visualization and use of graphical applications, including Microsoft Office (Power BI) and Tableau; major data science languages, such as R and Python; managing and margining of disparate data sources, preferably through Python, or SQL; statistical analysis; and data mining algorithms.
- Should have prior experience with large data Multi\-INT analytics, ML, and automated predicative analytics.
Requirements:
Minimum Qualifications:
- 3\+ years of experience designing, developing, operationalizing, and maintaining complex data applications
- 3\+ years of experience with raw coding of Python or Java, PostgreSQL or similar databases
- 3\+ years of experience developing code for data manipulation, data analysis, or data modeling
- A Current CompTIA Security\+ CE
- Bachelor’s degree or equivalent experience
- Experience creating and integrating code for retrieving, parsing, and processing data across multiple systems or applications
- Experience developing scripts and programs for converting various types of data into usable formats and supporting project teams to scale, monitor, and operate data platforms in support of specific mission goals
- Experience in the development of algorithms leveraging Python and SQL
- Experience with Python data science and visualization packages
- Experience with leading the development of solutions to complex problems
- Experience working within a TS environment with onsite development tools
- Experience with database administration or data analytics
- Experience with structured and unstructured data
- Must be a U.S. Citizen
- Must have and be capable of maintaining a U.S. Department of Defense (DoD) TS/SCI with a CI Polygraph clearance
Preferred Qualifications:
- 12\+ years of experience in Extract, Transform, and Load (ETL) processes
- 7\+ years of experience designing, developing, operationalizing, and maintaining complex data applications
- 7\+ years of experience with raw coding of Python or Java, PostgreSQL or similar databases
- 7\+ years of experience developing code for data manipulation, data analysis, or data modeling
- 3\+ years of experience with AI and ML implementation and development
- Experience with DevSecOps tools, including GitHub
- Experience working within the Intel Community
- Experience with a cloud environment, including AWS, Microsoft Azure, or Google Cloud
- Experience with Streamlit, Tkinter
- Experience with Agile software development
To Apply for this Position:
You must have the Minimum Qualifications in your resume to be selected as a candidate.
The salary range provided is a good faith estimate representative of all experience levels. E\&M considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate’s work experience, location, education/training, and key skills.
Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short\-term disability, long\-term disability, 401(k) match, flexible spending accounts, flexible work schedules, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective\-bargaining agreement.
This role is a U.S.\-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply.?
E\&M anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require E\&M to shorten or extend the application window.?
E\&M is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. E\&M provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans’ Readjustment Assistance Act.
As part of the federal government’s transition to the Trusted Workforce 2\.0 Continuous Vetting framework, the Defense Counterintelligence and Security Agency (DCSA) is expanding enrollment in the FBI Rap Back program for cleared industry personnel beginning April 1, 2026\.
Rap Back (Record of Arrest and Prosecution Back) is a service provided by the FBI that allows authorized agencies to receive notifications if there are updates to an individual’s criminal history record while they hold a security clearance. This supports continuous vetting of cleared personnel.
Per DCSA guidance, we are required to provide the following FBI privacy advisements to all cleared employees:
- FBI Privacy Act Notice
https://www.fbi.gov/how\-we\-can\-help\-you/more\-fbi\-services\-and\-information/compact\-council/privacy\-act\-statement
- Noncriminal Justice Applicant’s Privacy Rights
https://www.fbi.gov/how\-we\-can\-help\-you/more\-fbi\-services\-and\-information/compact\-council/guiding\-principles\-noncriminal\-justice\-applicants\-privacy\-rights
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At E&M Technologies, Inc., 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 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
E&M Technologies, Inc. AI Hiring
E&M Technologies, Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in Charlottesville, VA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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