Data Analyst

🔥 21 minutes ago

🇺🇸 United States – Remote

💵 $80k - $145k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📉 Data Analyst

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 0%

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LMI

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1961

📦 Logistics

🏥 Healthcare

🎖️ Defense

Logistics • Healthcare • Defense

LMI is a forward-thinking company that focuses on reimagining the path from insight to outcome through innovative solutions in various sectors, including applied AI and digital health. They provide advanced analytics, engineering support, and performance optimization across defense, health, and civilian markets, with a strong commitment to enhancing mission effectiveness for government clients. With a focus on collaboration and research, LMI aims to drive positive change through its diverse capabilities and partnerships.

📋 Description

• 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

• 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