Dataverse, Data Science Lead

LMI

. Lead analytical strategy for complex operational, workflow, technology, and programmatic questions .

Posted 10/26/2026full-timeRemote • United StatesSenior💰 $111,209 - $180,317 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in analytical strategy, data governance, and advanced analytics, with a strong focus on Microsoft Dataverse, Python, and SQL. Capable of leading complex analytical efforts and communicating findings effectively to diverse stakeholders.

Highest-signal resume keywords

Microsoft Dataverse ExpertiseAdvanced Analytics MethodsPython ProgrammingSQL ProficiencyData Governance Standards

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical AnalysisPredictive ModelingMachine LearningData Pipeline DesignData Quality ControlData Model AssessmentNatural Language ProcessingGenerative AI EvaluationCausal InferenceTime-Series Analysis

Soft Skills

Technical LeadershipEffective CommunicationMentoring

Tools & Technologies

Power BIDatabricksSnowflakeAzureAWSGoogle Cloud PlatformDataverse QueriesAnalytical PipelinesReusable NotebooksSQL Scripts

Certifications & Qualifications

Master's DegreeDoctoral Degree (Preferred)Advanced Technical Certifications (Preferred)

Industry Keywords

Federal HealthHealthcareRegulated Data EnvironmentsComplex Mission-Driven OrganizationsData GovernanceResponsible AIAnalytical StandardsData Quality ChecksEvidence LimitationsAnalytical Methodologies

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Lead analytical strategy for complex operational, workflow, technology, and programmatic questions
Serve as subject-matter expert for Microsoft Dataverse data structures, management practices, analytical use cases, and integrations
Assess data readiness, quality, completeness, sensitivity, lineage, authoritative sources, access, and fitness for intended measures
Design and maintain reporting-ready Dataverse data models, tables, relationships, transformations, validation rules, business logic, and analytical pipelines
Develop, review, and apply statistical models, machine-learning methods, exploratory analyses, predictive analytics, and AI/LLM evaluations
Perform hands-on analysis using Python, SQL, Dataverse queries, and related tools
Partner with program, operational, technical, evaluation, and data-governance stakeholders on analytical foundations and evidence limitations
Translate complex findings into concise, actionable products for technical and nontechnical decision-makers
Lead feature definition, model selection, validation, error analysis, sensitivity testing, bias and fairness assessments, and interpretation
Establish analytical standards and governance for reproducibility, peer review, validation, documentation, version control, and data-quality checks
Create reusable notebooks, SQL scripts, code patterns, templates, Dataverse standards, data dictionaries, and quality checks
Coach analysts, data scientists, and technical teams
Coordinate with data engineering, platform, application-development, and Power Platform resources on pipelines, integrations, handoff, monitoring, security, and maintainability
Lead technical reviews of analytical plans, data models, methods, and results
Partner with data owners and governance stakeholders to improve definitions, provenance, access, quality, retention, and responsible-use controls
Support dashboards, reports, and self-service analytical products
Remain current on data governance, privacy, security, analytical methodologies, Dataverse capabilities, and responsible AI

Requirements

What you’ll need

U.S. citizenship required
Ability to obtain a CBP Background Investigation (BI) required; active CBP BI preferred
Master’s degree or higher in statistics, data science, operations research, public health, computer science, engineering, mathematics, or another related quantitative discipline
At least 9 years of progressive experience in applied data science, advanced analytics, statistical analysis, data engineering, or related analytical roles
Experience in federal health, healthcare, regulated-data environments, enterprise modernization, or similarly complex mission-driven organizations
Experience leading complex analytical efforts from problem definition through data assessment, analysis, validation, interpretation, and communication
Strong hands-on experience with Python and SQL
Experience designing and assessing data models, data pipelines, data-quality controls, validation logic, data lineage, authoritative data sources, and reporting-ready datasets
Experience with advanced analytical methods including predictive modeling, time-series analysis, causal inference, natural-language processing, machine learning, generative AI, or large language model evaluation
Experience with modern data, analytics, and visualization platforms such as Microsoft Dataverse, Databricks, Snowflake, Power BI, Azure, AWS, Google Cloud Platform, or comparable technologies
Ability to communicate analytical methods, results, uncertainty, assumptions, bias, data-quality concerns, and limitations clearly
Experience establishing or applying analytical standards for reproducibility, peer review, documentation, version control, quality assurance, validation, and responsible use of data and AI
Technical leadership experience, including mentoring analysts or data scientists, leading peer reviews, developing reusable analytical standards or assets, or managing complex analytical workstreams
20% travel, potentially involving remote and rugged areas
Preferred: doctoral degree; healthcare, federal health, clinical analytics, public health, or operations research experience; analytics or machine-learning operationalization; NLP, generative AI, or LLM solution evaluation; Dataverse and Microsoft Power Platform experience; advanced technical certifications; recognized technical leadership evidence