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About This Role
Overview:
LMI is seeking a Data Scientist to assist with Army data model, pipeline, and visualization development. This Data Scientist will create data models working from mock visualizations and raw data inputs; leverage python, PySpark, Foundry, and code repositories to develop an architecture plan; extract, transform, and load data into Vantage; and use Vantage workshops to develop visualizations. Active Secret clearance required. Location: Open to Remote. Local preferred at client site in Crystal City VA or Aberdeen MD.
LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial\-grade platforms and mission\-ready AI to federal agencies at commercial speed.
Leveraging our mission\-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.
Responsibilities:
Core Responsibilities:
- Data Engineering: Extract, transform, and load data using big data platforms like Palantir Foundry, loading data into data visualizations, transforming data using python and low code data pipeline building tools.
- Workflow Development: Build low\-code no\-code applications that allow end users to see real\-time readiness dashboards.
- Data Integration: Automate and clean the ingestion of data from legacy systems so it’s useful for decision\-making.
- Predictive Analytics: Create models to forecast equipment failures, personnel shortages, or budget overruns.
- PI Planning: Help with feature writing, task decomposition, acceptance criteria, user stories, and processes for extracting dependencies from descriptive models.
- Ad\-Hoc Reporting: Quickly answer critical information requirements by pulling data across different domains, ability to gather customer requirements and deliver a comprehensive statistical analysis to answer their questions.
General Responsibilities:
- Support project management over data activities to ensure timely delivery in a reactive and agile environment.
- Engage technical and functional program SMEs, and key data stakeholders to understand business processes, analytical requirements, key data elements, data protection guidelines, etc.
- Define and develop techniques to integrate, consolidate, and structure data for analytical use.
- Create data pipelines in python, going from raw data to data products needed for visualization or other analytics products.
- Understand and analyze complex and organization\-specific datasets.
- Transform data and analysis into informative visualizations and interactive dashboards using open\-source and commercially available visualization and dashboard tools.
- Propose alternative designs and processes to manage various types of data using both standard and custom tables and fields.
- Provide recommendations for improvements in data strategies to include changes to governance, stewardship, integration, and standardization.
Qualifications:
Qualifications:
- Bachelor's degree in data science, mathematics, statistics, economics, computer science, engineering, or other related business or quantitative discipline preferred. Relevant experience considered in lieu of a degree.
- Active DoD Secret clearance required. Must have at a minimum, an Interim Secret Clearance.
- 10 years of experience with Python, and strong experience with PySpark or similar big data libraries.
- Knowledge of Army Vantage and data environment standardization.
- Experience working with data analysis tools including object\-oriented programming (Python, Java, SQL, TypeScript), computational analysis tools (R, MATLAB), and associated data science libraries (scikit\-learn).
- Experience developing and implementing statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics as well as to develop statistical tests to make data\-driven recommendations and decisions
- Experience creating meaningful data visualizations and interactive dashboards using platforms such as Tableau, Qlik, Power BI, RShiny, plotly, or d3\.js to communicate findings and relate them back to how insights create business impact
- Experience with data modeling, building "objects" (e.g., "Soldier," "Tank," "Unit") and defining how they relate to one another in the Foundry environment.
- Strong background in DevOps and Software Engineering, including GitLab CI/CD, Infrastructure as Code, and implementing Quality Assurance practices like Test\-Driven Development (TDD) and unit testing (Pytest).
- Proven ability to design and scale Medallion Lakehouse Architectures, implement Data Mesh concepts, and utilize Dimensional Modeling (Star Schemas) on cloud platforms like Databricks or DoD Advana.
- Innovative problem solving and root cause identification skills.
- Superior communication skills, both oral and written.
- Able to work effectively at all levels of an organization.
- Must be a team player and able to work with and through others.
- Ability to influence and align others and move toward a common vision or goal.
- Federal government agency experience required
Desired:
- Master's degree (preferred) in data science, mathematics, statistics, economics, computer science, engineering, or other related business or quantitative discipline.
- Experience with authoritative data sources, including IPPS\-A / MILPDS, GCSS\-Army, GFEBS, DTMS / MEDPROS.
- Experience with Army Vantage
- Experience with efforts focused on understanding interdependencies of programs within an organization.
Travel: Minimal, less than 10%
Location: Open to Remote. Local preferred at client site in Crystal City VA or Aberdeen MD.
Target salary range: $111,426 \- $150,000
Disclaimer: The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances.
\#LI\-SH1
Applicants must meet eligibility requirements for a U.S. Government security clearance. Only US Citizens are eligible for a security clearance. For this position, LMI will only consider applicants with security clearances or applicants who are eligible for security clearances, due to the nature of the work.
Job Locations: US\-Remote US\-MD\-Belcamp US\-VA\-Crystal City
Salary Context
This $111K-$150K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At LMI, 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. This role's midpoint ($130K) sits 32% below the category median. Disclosed range: $111K to $150K.
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.
LMI AI Hiring
LMI has 10 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, MLOps Engineer, AI Software Engineer. Positions span McLean, VA, US, Falls Church, VA, US, Remote, US. Compensation range: $150K - $195K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 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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