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About This Role
Overview:
LMI is seeking a Data Scientist to support advanced analytical efforts focused on future space architectures, including sustained space maneuver and resilient orbital operations. In this role, you will develop and apply quantitative methods, statistical models, and data\-driven techniques to analyze outputs from modeling, simulation, and trade studies.
You will work closely with analysts, software developers, and systems engineers to transform large volumes of simulation and scenario data into insightful, decision\-relevant results that inform force design and capability development.
This position is ideal for a data scientist who thrives in analytically complex, low\-data, high\-uncertainty environments, and who is comfortable working with both structured and exploratory analytical problems.
This is an on\-site role in Colorado Springs, CO.
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:
Responsibilities* Develop and apply statistical, mathematical, and computational methods to analyze modeling and simulation outputs
- Support execution of trade studies and design\-of\-experiments approaches, including parameter sweeps and sensitivity analysis
- Process and analyze large\-scale simulation datasets, identifying key drivers, trends, and relationships
- Develop models to support uncertainty quantification, risk analysis, and performance assessment
- Collaborate with trade space analysts to ensure analytical methods align with study objectives and decision needs
- Work with backend developers to integrate analytical workflows into RAPTR® and related platforms
- Develop data pipelines and scripts to support repeatable and scalable analysis
- Visualize analytical results and communicate findings to both technical and non\-technical stakeholders
- Contribute to technical reports, briefings, and recommendations supporting force design and architecture evaluation
- Explore advanced analytical techniques (e.g., surrogate modeling, optimization methods, or machine learning) where appropriate
Qualifications:
Required Qualifications* Bachelor’s degree in Data Science, Operations Research, Applied Mathematics, Physics, Computer Science, or a related quantitative field
- 5\+ years of experience in data science, quantitative analysis, or modeling \& simulation environments
- Strong proficiency in Python (preferred) or similar analytical programming languages
- Experience with statistical analysis, including regression, hypothesis testing, and multivariate analysis
- Experience working with simulation or experimental data
- Familiarity with data analysis libraries such as NumPy, Pandas, and SciPy
- Experience designing and executing data\-driven analytical workflows
- Ability to operate in loosely defined problem spaces and adapt analytical approaches as needed
- Strong problem\-solving and critical thinking skills
- Excellent communication and collaboration skills
Preferred Qualifications* Experience supporting modeling \& simulation, wargaming, or defense\-related analytical efforts
- Familiarity with design of experiments (DOE), optimization techniques, or Monte Carlo methods
- Experience developing surrogate models or reduced\-order models for complex simulations
- Exposure to space domain problems (e.g., orbital systems, satellite operations, mission analysis)
- Experience with data visualization tools such as Matplotlib, Plotly, or similar
- Familiarity with cloud\-based or distributed computing environments
- Advanced degree (M.S. or Ph.D.) in a relevant quantitative field
- Active TS/SCI clearance
Security Clearance Requirements
Candidate must be eligible to obtain a TS/SCI clearance and willingness to obtain a CI Poly. Candidates with an active TS/SCI or TS with SCI eligibility will be given preference.
Target salary range: $111,426 \- $192,890
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
Job Locations: US\-CO\-Colorado Springs
Salary Context
This $111K-$192K 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 ($152K) sits 21% below the category median. Disclosed range: $111K to $192K.
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.
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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