LMI is a new breed of digital solutions provider, disrupting the traditional govcon model with commercial-grade technology that is ready day-one. Driven by mission impact and backed by hard-earned customer trust, we help federal agencies move faster and smarter—delivering where it matters most. Experience and innovation fuel the what; expertise and precision drive the how.

Our commitment to our customers means staying ahead of the curve—and their mission needs. Leveraging cutting-edge platforms, mission-ready AI, and iterative collaboration, we deliver real results and meaningful transformation for the U.S. government. The outcome: agencies that are more agile, more responsive, and always ready for what’s next.

Overview:

LMI is seeking a Data Analyst to support Department of Veterans Affairs (VA) modernization initiatives. These full-time positions provide hands-on analytical capacity across AI-enabled use cases, operational improvement, workflow modernization, performance measurement, reporting, data-quality assessment, and decision support.

The Data Analysts will use SQL and Python as core day-to-day tools to acquire, prepare, validate, analyze, and automate data workflows. They will also support AI-enabled analysis and solution evaluation by preparing evaluation datasets, assessing AI-generated outputs, identifying data-quality and grounding issues, measuring performance, and helping teams distinguish useful AI-assisted insights from unsupported or unreliable results.

The role works closely with the Senior AI Lead, Data Science Lead, data integration, program evaluation, clinical informatics, delivery, and technical teams. These analysts are not expected to replace data scientists or AI engineers; they provide the scalable analytical execution, validation, and evidence needed to move multiple AI and modernization workstreams forward responsibly.

Candidates should independently execute well-scoped analyses and recurring analytical products. Candidates should own more complex multi-source and AI-enabled analyses, shape analytical approaches, review work, and advise stakeholders. Strong candidates combine practical AI experience with strong SQL and Python skills, disciplined documentation, data-quality judgment, and concise communication.

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:

• Support AI-enabled analytics and modernization use cases by preparing data, defining measures, evaluating outputs, and identifying data-quality, grounding, accuracy, bias, or reliability issues that could affect decisions.

• Use SQL and Python as primary analytical tools to extract, query, join, clean, transform, validate, analyze, and automate data across multiple approved sources.

• Build and maintain evaluation datasets, test cases, comparison analyses, and performance measures for AI/ML or generative AI capabilities under appropriate technical leadership.

• Profile enterprise, operational, workflow, program, survey, and AI-related data for availability, completeness, consistency, lineage, sensitivity, and fitness for intended use.

• Perform exploratory, descriptive, trend, cohort, segmentation, variance, before/after, and exception analysis appropriate to the decision need and available data.

• Build analysis-ready datasets, analytical tables, visualizations, dashboards, and concise decision-support products for technical and non-technical audiences.

• Translate stakeholder and AI-use-case questions into data requirements, metric definitions, filters, denominators, business rules, validation checks, and analytical outputs.

• Support retrieval-augmented generation (RAG), AI search, or knowledge-based use cases by assessing source coverage, authoritative data, metadata, retrieval quality, and content gaps.

• Reconcile discrepancies across source systems, AI outputs, reports, dashboards, or stakeholder calculations and determine the source and materiality of inconsistent results.

• Automate repeatable data preparation, quality checks, calculations, evaluation routines, and reporting using reusable SQL and Python code where practical.

• Partner with AI, data science, and engineering resources when work requires advanced modeling, model development, pipelines, APIs, new integrations, or production deployment.

• Perform quality assurance and reasonableness checks before findings are released; escalate data, AI-output, or methodology concerns that could materially change conclusions.

• Document sources, queries, Python code, transformations, assumptions, exclusions, evaluation logic, validation steps, and limitations so analytical work is reproducible and reviewable.

• For Level IV assignments, lead higher-complexity AI-enabled analysis, conduct peer review, mentor analysts, and represent the analytical team in stakeholder and technical discussions.

Required:

Bachelor's degree in data analytics, statistics, mathematics, economics, operations research, computer science, information systems, public health, engineering, or a related quantitative field; equivalent experience may be considered.

5+ years of relevant data analysis, business intelligence, performance analysis, reporting, or operational analytics experience. Level IV placement will generally require 7+ years plus independent ownership of complex analysis.

Strong hands-on SQL and Python skills are required. Candidates must be able to independently write, troubleshoot, and validate SQL queries and Python-based analytical workflows rather than relying primarily on no-code tools.

Demonstrated hands-on experience applying AI/ML or generative AI in analytical workflows is required, including experience evaluating AI-generated outputs, preparing evaluation data, measuring performance, or assessing data and evidence supporting AI-enabled solutions.

Experience preparing and validating multi-source data, including joins, transformations, duplicate handling, missing-data review, exception analysis, reconciliation, and source-to-output traceability.

Working knowledge of descriptive statistics, trends, distributions, baselines, comparisons, sampling concepts, and appropriate interpretation of observational and AI-assisted analysis.

Ability to translate ambiguous stakeholder or AI-use-case questions into clear analytical requirements and adjust the approach when available data cannot support the requested conclusion.

Strong data-quality judgment and ability to communicate definitions, assumptions, AI limitations, uncertainty, and analytical findings clearly to technical and non-technical audiences.

Experience maintaining reproducible analytical work through documented SQL/Python code, data dictionaries, version control, peer review, testing, or comparable quality practices.

Recommended certification: Microsoft Power BI Data Analyst, Microsoft Azure AI Fundamentals/AI Engineer, Databricks Data Analyst or Generative AI credential, Tableau Certified Data Analyst, or a comparable analytics/AI credential.

Ability to satisfy VA personnel vetting and applicable security, privacy, records-management, responsible-AI, and data-handling requirements.

Desired:

6+ years supporting enterprise, federal, healthcare, operational, program, or mission analytics; 8+ years is especially valuable for Level IV candidates.

Prior VA, VHA, federal health, or other federal analytics experience in a large, governed enterprise environment.

Experience with generative AI/LLMs, RAG, prompt/output evaluation, AI quality measurement, human-in-the-loop review, or analytics supporting AI-enabled workflow modernization.

Advanced experience with SQL Server, Python data libraries, Power BI, Microsoft Fabric, Databricks, Snowflake, Tableau, or comparable enterprise analytics technologies.

Experience with APIs, semantic models, data warehouses/lakehouses, ETL/ELT outputs, vector/search data, governed datasets, or coordination with data engineering teams.

Experience supporting pilot analysis, implementation measurement, program evaluation, benefits tracking, AI/ML evaluation, or operational performance measurement.

Additional certification or training in AI/ML, generative AI, cloud analytics, Databricks, Fabric, SQL/data engineering, statistics, Power BI, Tableau, or Lean Six Sigma is preferred.

For Level IV candidates, experience with peer review, mentoring, analytical standards, and independently shaping complex AI-enabled analyses is strongly preferred.