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Xenith Solutions

SDVOSB government IT, cybersecurity, data engineering

Dataverse/Data Science Lead

Data Management +1Confirmed live in the last 24 hours
Full-timeWashington, DC, USA$120k - $130k /yr

About the Job

Requirements

  • At least 9 years of progressive experience in applied data science, advanced analytics, statistical analysis, data engineering, or related analytical roles, including work 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 of results 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, data pipelines, data-quality controls, validation logic, data lineage, authoritative data sources, and reporting-ready datasets.
  • Experience with one or more 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 to leadership and other decision-makers.
  • 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.
  • A 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 is required.
  • Ability to obtain a CBP Background Investigation is required.

Responsibilities

  • Lead the analytical strategy for complex operational, workflow, technology, and programmatic questions, aligning analyses 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 analytical and reporting use cases, including data quality, completeness, sensitivity, lineage, authoritative sources, access considerations, 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.
  • Perform hands-on analysis using Python, SQL, Dataverse queries, and related tools; troubleshoot complex data, integration, transformation, reporting, and modeling issues.
  • Partner with program, operational, technical, evaluation, and data-governance stakeholders to establish appropriate baselines, denominators, comparisons, outcomes, performance measures, and evidence limitations.
  • Translate complex analytical findings into concise, actionable products for technical and nontechnical decision-makers, 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 analytical standards and governance practices for reproducibility, peer review, validation, documentation, version control, data-quality checks, and transparent communication of analytical assumptions and limitations.
  • Create reusable notebooks, SQL scripts, code patterns, analytical templates, Dataverse configuration standards, data dictionaries, and quality checks to improve consistency and accelerate future work.
  • Coach analysts, data scientists, and technical teams on data-management practices, 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 data-pipeline design, integrations, model handoff, monitoring requirements, performance expectations, security considerations, and maintainability.
  • Lead technical reviews of analytical plans, data models, methods, and results to ensure data, validation approach, assumptions, and interpretation are appropriate for customer decisions.
  • Partner with data owners and governance stakeholders to improve data definitions, provenance, access patterns, quality expectations, retention practices, and responsible-use controls when recurring analysis identifies systemic data issues.
  • Support development of dashboards, reports, and self-service analytical products using approved reporting and visualization tools, ensuring outputs are accurate, documented, and fit for operational use.
  • Remain current on applicable data-governance policies, privacy and security requirements, analytical methodologies, Microsoft Dataverse capabilities, and responsible AI practices.

Desired Qualifications

  • A 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 operational or programmatic decisions.
  • Experience operationalizing analytics or machine-learning solutions through repeatable data pipelines, model monitoring, MLOps practices, model lifecycle management, or collaboration with production engineering and platform teams.
  • Experience designing, implementing, or evaluating natural-language processing, generative AI, or large language model solutions, including evaluation methodologies, error analysis, bias or fairness assessment, and responsible-use considerations.
  • Experience working with Dataverse and the Microsoft Power Platform, including Dataverse 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 leadership of enterprise-scale analytical initiatives.
  • Active CBP Background Investigation is preferred.

About the Company

Xenith Solutions

SDVOSB government IT, cybersecurity, data engineering

Xenith Solutions provides government-focused technology and consulting services, delivering enterprise IT, cybersecurity, data engineering, and systems integration to federal agencies. Its offerings include Agile and LEAN software development, IT modernization, cloud engineering, and enterprise data management to support national security missions. The firm uses government contracts and vehicles like the GSA MAS, and emphasizes its SDVOSB status, defense leadership experience, and certifications (ISO 9001, 27000, 20000; CMMI Level 3). Its goal is to provide mission-ready IT and cybersecurity capabilities that help federal customers modernize, protect, and manage data and systems critical to national security.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Not listed
Founded
2019

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