Data Analyst

LMI

. 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 .

Posted 10/28/2026full-timeRemote • United StatesMid-LevelSenior💰 $80,000 - $145,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in SQL and Python for data extraction, transformation, and analysis, while applying AI/ML methodologies to enhance analytical workflows. Proven ability to translate complex stakeholder requirements into actionable data insights and maintain high standards of data quality and documentation.

Highest-signal resume keywords

SQLPythonAI/ML ApplicationData Quality AssessmentData Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data PreparationData ValidationStatistical AnalysisPerformance MeasurementData TransformationExploratory AnalysisException AnalysisData ReconciliationAnalytical Table CreationVisualization Development

Soft Skills

Clear CommunicationStakeholder EngagementMentoringJudgment in Data QualityProblem-Solving

Tools & Technologies

Microsoft Power BIDatabricksTableauSQL ServerSnowflakeMicrosoft FabricAPIsData WarehousesETL/ELTGenerative AI

Certifications & Qualifications

Microsoft Power BI Data AnalystMicrosoft Azure AI FundamentalsDatabricks Data AnalystGenerative AI CredentialTableau Certified Data Analyst

Industry Keywords

Data AnalyticsBusiness IntelligenceOperational AnalyticsFederal Health AnalyticsHealthcare AnalyticsAI-Enabled SolutionsPerformance AnalysisData GovernanceQuantitative AnalysisResponsible AI

Tech Stack

Tools & technologies

AzureCloudETLPythonSnowflakeSQLTableau

About the role

Key responsibilities & impact

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
Use SQL and Python to extract, query, join, clean, transform, validate, analyze, and automate data across approved sources
Build and maintain evaluation datasets, test cases, comparison analyses, and performance measures for AI/ML or generative AI capabilities
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
Build analysis-ready datasets, analytical tables, visualizations, dashboards, and decision-support products
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
Automate repeatable data preparation, quality checks, calculations, evaluation routines, and reporting using reusable SQL and Python code
Partner with AI, data science, and engineering resources on advanced modeling, pipelines, APIs, integrations, and production deployment
Perform quality assurance and reasonableness checks; escalate data, AI-output, or methodology concerns
Document sources, queries, Python code, transformations, assumptions, exclusions, evaluation logic, validation steps, and limitations
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

Requirements

What you’ll need

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
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 interpretation of observational and AI-assisted analysis
Ability to translate ambiguous stakeholder or AI-use-case questions into clear analytical requirements
Strong data-quality judgment and ability to communicate definitions, assumptions, AI limitations, uncertainty, and analytical findings clearly
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
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
Desired: 6+ years supporting enterprise, federal, healthcare, operational, program, or mission analytics; 8+ years especially valuable for Level IV candidates
Desired: Prior VA, VHA, federal health, or other federal analytics experience in a large, governed enterprise environment
Desired: Experience with generative AI/LLMs, RAG, prompt/output evaluation, AI quality measurement, human-in-the-loop review, or analytics supporting AI-enabled workflow modernization
Desired: Advanced experience with SQL Server, Python data libraries, Power BI, Microsoft Fabric, Databricks, Snowflake, Tableau, or comparable enterprise analytics technologies
Desired: Experience with APIs, semantic models, data warehouses/lakehouses, ETL/ELT outputs, vector/search data, governed datasets, or coordination with data engineering teams
Desired: Experience supporting pilot analysis, implementation measurement, program evaluation, benefits tracking, AI/ML evaluation, or operational performance measurement
Desired: 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 is strongly preferred