Interested in this Data Scientist role at LMI?
Apply Now →Skills & Technologies
About This Role
Overview:
LMI is seeking a Data Scientist to support a Special Operations Command (SOCOM) mission partner with advanced analytics, predictive modeling, natural language processing, and artificial intelligence and machine learning (AI/ML) product development.
The Data Scientist will analyze complex historical and operational datasets, convert data into machine\-learning\-ready formats, identify trends and predictive features, develop and validate statistical and machine learning models, and provide decision\-quality insights that support resource forecasting, operational planning, and modernization. This position will work as part of a cross\-functional data science product team to develop, integrate, govern, sustain, and document mission\-relevant applications, dashboards, models, and research products.
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 achieve mission success. *This position is on\-site at Fort Bragg and requires an active Secret security clearance with the ability to obtain a Top Secret clearance.*
Responsibilities:
- Analyze historical operational records, program execution data, and related datasets to identify trends, relationships, anomalies, and key features for predictive modeling.
- Clean, normalize, reconcile, label, and transform structured and unstructured data from multiple sources into traceable, machine\-learning\-ready datasets.
- Develop, test, and refine predictive models to forecast resource consumption and annual rate projections with a maximum error margin of 1%.
- Apply statistical analysis, feature engineering, time\-series forecasting, regression, ensemble methods, and other appropriate techniques to improve model accuracy, reliability, explainability, and operational usefulness.
- Establish model validation, back\-testing, sensitivity analysis, error analysis, and performance\-monitoring methods; document assumptions, limitations, risks, and sources of uncertainty.
- Develop natural language processing and generative AI solutions, including large language models tailored to approved business, operational, and intelligence use cases.
- Develop projects that automate or augment human cognitive workload and respond rapidly to emerging operational data and data science requirements.
- Collaborate with AI/ML engineers, data engineers, software developers, cybersecurity personnel, and mission stakeholders to integrate validated models and analytical outputs into secure web\-based applications and enterprise workflows.
- Support enterprise synchronization, integration, governance, security, sustainment, and adoption of data science and AI/ML products across multiple mission teams and stakeholder organizations.
- Translate complex analytical findings into clear, actionable insights and recommendations for technical teams, program managers, operational users, and senior mission\-partner leaders.
- Develop and maintain customer\-focused data science products, including applications, dashboards, analytical models, and research projects, through their full product life cycle.
- Produce analytical reports, dashboards, briefings, and decision\-support products that communicate trends, insights, model performance metrics, and recommendations.
- Maintain comprehensive documentation of data sources, methodologies, feature definitions, model logic, validation results, system dependencies, workflows, and repeatable analytical processes.
- Develop user guides, training materials, demonstrations, and knowledge\-transfer products sufficient for a qualified practitioner to assume future operation and sustainment of the application or capability.
- Provide rapid\-response analytical and product\-level staff augmentation based on changes in mission priorities and the operational environment.
Qualifications:
Required Qualifications
===========================
- Active Secret security clearance with the ability to obtain a Top Secret clearance.
- Ability to work on\-site at Fort Bragg, North Carolina.
- Bachelor’s degree in data science, statistics, mathematics, computer science, operations research, engineering, or a related quantitative field.
- Five or more years of professional experience applying data science, advanced analytics, statistical modeling, or machine learning to complex real\-world problems.
- Demonstrated experience developing and validating predictive models, including time\-series, regression, ensemble, or comparable forecasting methods.
- Advanced proficiency with Python and SQL and practical experience with common data science and machine learning libraries; proficiency with R or a comparable analytical language may substitute where appropriate.
- Strong knowledge of statistical analysis, feature engineering, model selection, hyperparameter tuning, cross\-validation, error analysis, and performance measurement.
- Experience preparing large, incomplete, inconsistent, structured, and unstructured datasets for repeatable analysis and model training.
- Experience with natural language processing, generative AI, large language models, or retrieval\-augmented generation in an applied environment.
- Ability to evaluate model performance against stringent accuracy requirements and clearly communicate tradeoffs, risks, assumptions, and limitations.
- Experience producing technical documentation, analytical reports, dashboards, briefings, and recommendations for technical and non\-technical stakeholders.
- Strong written and verbal communication skills and the ability to collaborate across data, engineering, software, security, governance, and operational teams.
- Ability to independently manage multiple priorities and deliver high\-quality analytical products in a fast\-paced, mission\-focused environment.
Preferred Qualifications
============================
- Master’s degree or doctorate in data science, statistics, mathematics, computer science, operations research, engineering, or a related quantitative field.
- Experience supporting SOCOM, U.S. Special Operations Forces, the Department of War, or another national security mission partner.
- Experience developing models for resource consumption, demand, readiness, utilization, program execution, or annual planning forecasts.
- Experience integrating analytical models into production web applications through APIs, services, containers, or reusable software components.
- Familiarity with MLOps, DevSecOps, model monitoring, version control, automated testing, and continuous integration and continuous delivery practices.
- Experience with secure cloud analytics environments such as AWS GovCloud or Azure Government and data visualization platforms such as Power BI or Tableau.
- Familiarity with Agile delivery methods and experience working in cross\-functional product or software development teams.
- Experience supporting data governance, model governance, application sustainment, user adoption, and knowledge transfer for government data products.
Target Competencies
=======================
- Mission Focus
- Analytical Rigor
- Technical Excellence
- Product Ownership
- Collaboration and Stakeholder Engagement
- Clear Communication
- Adaptability and Continuous Learning
Target Salary Range: $125,144 \- $195,591 *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\-NC\-Fort Bragg
Salary Context
This $125K-$195K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($160K) sits 17% below the category median. Disclosed range: $125K to $195K.
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.
LMI AI Hiring
LMI has 2 open AI roles right now. They're hiring across MLOps Engineer, Data Scientist. Positions span Remote, US, Fort Bragg, NC, US. Compensation range: $185K - $195K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.