Dataverse/Data Science Lead

Xenith Solutions•Washington, DC

9d•$120,000 - $130,000

About The Position

Xenith Solutions is seeking a Dataverse/Data Science Lead to join a multidisciplinary team supporting the Department of Homeland Security (DHS) Customs and Border Protection (CBP) Program Management Office. The role applies technical expertise to national security challenges through research, evaluation, and informed decision-making. The lead will combine advanced statistical expertise, strong coding skills, and mission awareness to assess data quality, validate assumptions, select analytical methods, and deliver accurate, defensible, actionable findings. The position requires U.S. citizenship and the ability to obtain a CBP Background Investigation (BI); an active CBP BI is preferred. It requires 20% travel, which may include remote and rugged areas. The role leads analytical strategy and serves as a subject-matter expert on Microsoft Dataverse data structures, management practices, analytical use cases, and integration with reporting, automation, and data-science solutions. It includes hands-on work with Python, SQL, Dataverse queries, data models, analytical pipelines, statistical and machine-learning methods, and AI/large-language-model evaluations. The lead establishes analytical standards and governance, mentors colleagues, conducts technical reviews, collaborates across engineering and program teams, and communicates findings, uncertainty, bias, assumptions, and limitations to stakeholders ranging from technical teams to executives. The required education is a master’s degree or higher in a quantitative discipline. Candidates must have at least nine years of progressive experience in applied data science, advanced analytics, statistical analysis, data engineering, or related analytical work, including relevant work in federal health, healthcare, regulated-data environments, enterprise modernization, or similarly complex mission-driven organizations.

Requirements

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 to technical and nontechnical stakeholders.
Strong hands-on experience using Python and SQL to develop, test, validate, and troubleshoot analytical workflows, data transformations, statistical models, and machine-learning solutions.
Experience designing and assessing data models, pipelines, data-quality controls, validation logic, data lineage, authoritative data sources, and reporting-ready datasets.
Experience with one or more advanced 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 to leadership and decision-makers.
Experience establishing or applying standards for reproducibility, peer review, documentation, version control, quality assurance, validation, and responsible use of data and AI.
Technical leadership experience, such as mentoring analysts or data scientists, leading peer reviews, developing reusable standards or assets, or managing complex analytical workstreams.
Master’s degree or higher in statistics, data science, operations research, public health, computer science, engineering, mathematics, or another related quantitative discipline.
U.S. citizenship and ability to obtain a CBP Background Investigation (BI).

Nice To Haves

Active CBP Background Investigation (BI).
Doctoral degree in statistics, data science, operations research, public health, computer science, engineering, economics, epidemiology, or a related quantitative discipline.
Experience supporting healthcare, federal health, clinical analytics, public health, operations research, or other domains where data context, quality, and interpretation materially affect decisions.
Experience operationalizing analytics or machine-learning solutions using repeatable data pipelines, model monitoring, MLOps practices, model lifecycle management, or collaboration with production engineering and platform teams.
Experience designing, implementing, or evaluating NLP, generative AI, or large language model solutions, including evaluation methods, error analysis, bias or fairness assessment, and responsible-use considerations.
Experience with Dataverse and Microsoft Power Platform, including table design, relationships, security roles, integrations, data management, reporting, and Power BI analytics.
Advanced certifications in cloud machine learning, data science, analytics, data engineering, Databricks, Azure, AWS, Google Cloud Platform, Power Platform, or comparable technologies.
Evidence of recognized technical leadership through publications, conference presentations, internal analytical standards, training development, mentoring, peer-review leadership, or enterprise-scale analytical initiatives.

Responsibilities

Lead analytical strategy for complex operational, workflow, technology, and programmatic questions, aligned with customer priorities and decision needs.
Serve as the lead subject-matter expert for Microsoft Dataverse data structures, data management practices, analytical use cases, and integration with reporting, automation, and data-science solutions.
Assess data readiness for analytics and reporting, including 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/large-language-model evaluations as appropriate.
Conduct hands-on analysis using Python, SQL, Dataverse queries, and related tools; troubleshoot data, integration, transformation, reporting, and modeling issues.
Partner with program, operational, technical, evaluation, and data-governance stakeholders to establish baselines, denominators, comparisons, outcomes, performance measures, and evidence limitations.
Translate analytical findings into actionable products for technical and nontechnical decision-makers, clearly communicating uncertainty, data-quality issues, assumptions, bias, and causal limitations.
Lead feature definition, model selection, validation strategies, error analysis, sensitivity testing, bias and fairness assessments, and interpretation for advanced analytics and machine-learning efforts.
Establish standards and governance for reproducibility, peer review, validation, documentation, version control, data-quality checks, and transparent communication of assumptions and limitations.
Create reusable notebooks, SQL scripts, code patterns, analytical templates, Dataverse configuration standards, data dictionaries, and quality checks.
Coach analysts, data scientists, and technical teams on data management, analytical methods, quality assurance, peer review, and responsible use of data and AI-enabled capabilities.
Coordinate with data engineering, platform, application-development, and Power Platform resources on pipelines, integrations, model handoff, monitoring, performance, security, and maintainability.
Lead technical reviews of analytical plans, data models, methods, and results to assess whether the data, validation approach, assumptions, and interpretation serve the customer’s decision.
Partner with data owners and governance stakeholders to improve data definitions, provenance, access patterns, quality expectations, retention, and responsible-use controls.
Support development of dashboards, reports, and self-service analytical products using approved reporting and visualization tools, ensuring they are accurate, documented, and fit for operational use.
Stay current on data-governance policies, privacy and security requirements, analytical methods, Microsoft Dataverse capabilities, and responsible AI practices.