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LMI

Data Scientist - Clearance Required

Pittsburgh, PA, United States

Target Salary Range: $130,000 - $180,000

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Job details

Pay: Target Salary Range: $130,000 - $180,000
Apply by: Nov 2, 2027
Posting language: English
Posted: Nov 2, 2026

Original job description

Overview

LMI is seeking a skilled Data Scientist to join our team and contribute to developing innovative analytics capabilities in support of the Army's AI Integration Center (AI2C). This role focuses on building predictive models, surfacing data-driven insights, and translating complex analytical outputs into actionable recommendations that advance the Army's AI strategy. The ideal candidate brings strong foundations in predictive analytics and machine learning, with the ability to communicate findings clearly to both technical and non-technical stakeholders.

This position offers the opportunity to work at the intersection of data science and national defense - applying cutting-edge analytical methods to real-world Army challenges spanning talent management, equipment sustainment, recruiting, and training. Candidates should be self-directed, collaborative, and comfortable operating in a fast-paced, mission-driven environment.

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:

Design, develop, and deploy predictive and prescriptive models that support Army decision-making across sustainment, talent management, and recruiting domains
Develop, implement, and maintain analytical models and simulations to evaluate complex systems, operational processes, mission effectiveness, and alternative courses of action.
Design and execute discrete-event, agent-based, stochastic, and Monte Carlo simulations using Python libraries such as SimPy, Mesa, NumPy, and SciPy to assess system behavior, operational performance, and outcomes under uncertainty.
Apply operations research techniques, including mathematical programming, optimization, stochastic modeling, queuing theory, and decision analysis, to address complex operational and resource allocation challenges.
Formulate and solve linear, nonlinear, integer, and mixed-integer optimization problems using Python packages such as Pyomo, PuLP, SciPy.optimize, OR-Tools, and CVXPY to evaluate scheduling, logistics, resource allocation, and operational planning alternatives.
Conduct quantitative trade studies, sensitivity analyses, scenario-based assessments, and what-if analyses to identify key performance drivers, evaluate operational alternatives, and inform decision-making.
Develop statistical and probabilistic models using NumPy, SciPy, pandas, statsmodels, and PyMC to characterize system behavior, quantify uncertainty, and evaluate operational risks.
Design and execute computational experiments, including design of experiments (DOE), parameter sweeps, Monte Carlo analysis, and sensitivity testing, to assess model performance and robustness across operational scenarios.
Perform model verification, validation, calibration, and uncertainty quantification using empirical data, statistical testing, and subject matter expertise to establish confidence in analytical results.
Integrate, transform, and analyze structured and unstructured data from multiple sources using pandas, NumPy, Polars, and associated Python data-processing libraries to support modeling, simulation, and optimization workflows.
Develop reusable, modular, and reproducible Python-based analytical tools and computational workflows, leveraging software development best practices, version control, automated testing, and documented methodologies.
Develop analytical visualizations, interactive dashboards, and decision-support tools using Matplotlib, Seaborn, Plotly, and Streamlit to communicate simulation outputs, optimization results, uncertainty, and operational tradeoffs.
Implement monitoring, logging, and troubleshooting to ensure system reliability and availability
Synthesize and communicate analytical findings, model assumptions, limitations, uncertainties, and actionable recommendations through technical documentation, reports, briefings, and presentations to technical and nontechnical decision-makers.
Build and maintain data pipelines and analytical workflows using Python, PySpark, and/or R, integrating with Army platforms such as Palantir Foundry and Army Vantage
Collaborate closely with UX/UI designers and software developers to integrate data science capabilities into user-facing applications
Knowledge of containerization tools (e.g. Docker, Docker-Compose, Kubernetes, etc.)
Provide technical leadership and mentorship, growing team capabilities at the intersection of data science and mission-focused application development
Engage with Army managers and subject matter experts to understand mission requirements, validate model assumptions, and build trust in analytical outputs
Document models, methodologies, and analytic processes in accordance with Army and DoD standards

Percentage of Travel Required: 10%

Qualifications

Minimum Qualifications:

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Operations Research, or a related quantitative field, or equivalent work experience
3–5 years of experience in data science or applied analytics, with demonstrated focus on predictive modeling and machine learning
Proficiency in Python (pandas, numpy, scikit-learn, statsmodels) and/or R for data manipulation, statistical analysis, and model development
Experience building and validating supervised and unsupervised ML models (regression, classification, clustering, time-series forecasting)
Experience with data visualization tools and libraries (Plotly, D3.js, Recharts, Tableau, or similar) to communicate analytical findings effectively
Experience with Palantir Foundry or comparable enterprise data platforms for data integration, pipeline development, and application support
Familiarity with Git, Agile methodologies, and collaborative development practices
Ability to communicate complex analytical concepts clearly to non-technical stakeholders and influence decisions through data
Active Secret Clearance required

Additional Preferred Qualifications:

Knowledge of Natural Language Processing (NLP) techniques and applications
Hands-on deployment experience across multiple environments and platforms
Background working in strategic planning or consultant environments supporting government or DoD clients
Proven track record of expanding technical scope or footprint with government customers
Knowledge of the Army software development process and its technologies.
7+ years of directly related experience.
Familiarity with human-centered design principles and integrating analytical outputs into user-facing applications
Master’s degree in Computer Science, Software Engineering, Information Systems, or related field.

Target Salary Range: $130,000 - $180,000

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.

Job Locations US-PA-Pittsburgh

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More about this job

Skills

Machine learning
Decision making
Python
NumPy
SciPy
Research
Scheduling
Logistics
ESRI What if?
pandas
Unstructured data
Process data
Software development
Test automation
Troubleshooting
Communicate analytical insights
Documentation
Presentation skills
PySpark
R
Docker
Kubernetes
Leadership
Statistics
Computer science
Scikit-learn
ML
Forecasting
Data visualization
Tableau
Git
Agile
Natural language processing
Strategic planning
Design principles

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