Data Analyst

LMI•Remote,

12d•$80,000 - $145,000•Remote

About The Position

LMI is seeking Data Analysts to support Department of Veterans Affairs modernization initiatives. These full-time roles provide hands-on analytical capacity for AI-enabled use cases, operational improvement, workflow modernization, performance measurement, reporting, data-quality assessment, and decision support. Analysts use SQL and Python to acquire, prepare, validate, analyze, and automate data workflows. They 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 collaborates with the Senior AI Lead, Data Science Lead, data integration, program evaluation, clinical informatics, delivery, and technical teams. Analysts provide analytical execution, validation, and evidence to advance AI and modernization workstreams; they are not expected to replace data scientists or AI engineers. Candidates should be able to execute well-scoped analyses and recurring products independently. More senior candidates may own complex multi-source and AI-enabled analyses, shape analytical approaches, review work, and advise stakeholders. LMI is a digital solutions provider focused on accelerating government impact through innovation and speed. It develops technology, prototypes, commercial-grade platforms, and mission-ready AI for federal agencies. LMI serves the defense, space, healthcare, and energy sectors and is headquartered in Tysons, Virginia.

Requirements

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 generally requires 7+ years plus independent ownership of complex analysis.
Strong hands-on SQL and Python skills, including the ability to independently write, troubleshoot, and validate SQL queries and Python-based analytical workflows rather than relying primarily on no-code tools.
Hands-on experience applying AI/ML or generative AI in analytical workflows, including 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 clearly communicate definitions, assumptions, AI limitations, uncertainty, and analytical findings 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.
Ability to satisfy VA personnel vetting and applicable security, privacy, records-management, responsible-AI, and data-handling requirements.

Nice To Haves

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 comparable analytics/AI credential.
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.
For Level IV candidates, experience with peer review, mentoring, analytical standards, and independently shaping complex AI-enabled analyses.

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 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 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.