Senior Data Scientist – Statistical Modeling, AI/ML

Aptive Resources

Senior Data Scientist at Aptive Resources focusing on statistical modeling, AI/ML, and data governance for VA healthcare. This full-time role supports EHR modernization and analytics for federal health initiatives.

Posted 7/14/2026full-timeRemote • Virginia • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced proficiency in R for statistical computing and analytics, with strong skills in AI/ML evaluation, data governance, and quality assurance in healthcare environments. Capable of translating complex data insights into actionable metrics and ensuring compliance through effective documentation and communication.

Highest-signal resume keywords

Advanced R ProficiencyAI/ML EvaluationData Governance ExperienceStrong SQL SkillsStatistical Modeling Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical ModelingMachine LearningData AnalysisStatistical DistributionsMatrix AlgebraTime-Series ModelingNatural Language ProcessingBayesian MethodsData ValidationReproducible Analytics

Soft Skills

Excellent CommunicationDocumentation SkillsCollaboration

Tools & Technologies

GitGitHubR PackagesDatabricksAzureServiceNowCorporate Data Warehouse

Certifications & Qualifications

Public Trust Clearance

Industry Keywords

Healthcare AnalyticsFederal HealthVA ExperienceData Quality AssuranceHealth Informatics

Tech Stack

Tools & technologies

AzurePythonServiceNowSQL

About the role

Key responsibilities & impact

Lead statistical modeling, AI/ML evaluation, and analytic decision support for VA EHR modernization, operational reporting, and executive-facing analytics workstreams.
Design, evaluate, and explain statistical and machine learning models, including distributional assumptions, matrix-based methods, dimension reduction, clustering, NLP, simulation, time-series modeling, Bayesian methods, and model limitations.
Assess model quality, reliability, bias, drift, and operational usefulness; identify when an analytical approach is not statistically valid or is not appropriate for the available data.
Serve as a data governance and data quality assurance point of contact, proactively identifying defects, gaps, anomalies, reporting inconsistencies, data capture issues, and root causes across complex datasets and reporting processes.
Develop, maintain, and document reproducible R-based analytics, R packages, scripts, pipelines, dashboards, and governed reporting outputs.
Integrate and validate data from enterprise healthcare, operational, EHR, ServiceNow, Corporate Data Warehouse (CDW), Databricks/Azure, and other data sources to support reliable program reporting.
Translate stakeholder questions into actionable metrics, KPI definitions, data validation rules, dashboard requirements, quality checks, and recurring reporting products for PMO, functional, and executive audiences.
Advise on data governance, provenance, metadata, versioning, access control, code review, documentation, and production standards for analytics teams.
Use Git/GitHub and documentation workflows to support version control, collaborative development, pull requests, code review, reproducibility, and transparent analytical delivery.
Partner with analysts, data engineers, program managers, and VA stakeholders to move ad hoc analyses into governed, repeatable, auditable data products and reporting processes.
Support Agile/SAFe delivery by helping define features, user stories, acceptance criteria, sprint-ready analytics work, and backlog priorities.

Requirements

What you’ll need

5+ years of applied data science, statistics, machine learning, health informatics, data engineering, or analytics experience in complex enterprise or healthcare environments; federal health, VA, or VHA experience strongly preferred.
Advanced degree or equivalent experience in statistics, biostatistics, mathematics, data science, computer science, public health, health informatics, or a related quantitative field.
Strong theoretical and applied statistics foundation, including statistical distributions, mathematical modeling, matrix/linear algebra concepts, inference, uncertainty, and model diagnostics.
Demonstrated AI/ML experience with the ability to evaluate when models are appropriate and reliable, and to diagnose conditions under which models underperform, drift, become biased, or fail.
Advanced proficiency in R for statistical computing, modeling, reproducible analytics, and package/tool development; experience developing or maintaining R packages is strongly valued.
Strong SQL skills for querying, joining, validating, and transforming large datasets; working knowledge of Python preferred.
Hands-on experience with Git and GitHub for version control, collaboration, code review, and reproducible delivery.
Experience supporting data governance, data quality assurance, stewardship, metadata, lineage, provenance, versioning, access control, and analytics documentation.
Excellent communication and documentation skills. Able to brief executives, translate technical findings into operational implications, and guide teams toward clean, auditable, compliant analytics.
U.S. Citizenship and ability to obtain a Public Trust clearance.

Benefits

Comp & perks

Health insurance
Professional development opportunities
Flexible work arrangements