Dataverse / Data Science Lead (CBP)
Summary
Lead advanced analytics and data science initiatives using Python, SQL, and Microsoft Dataverse to support federal operations. This role involves data modeling, machine learning, and communicating insights to diverse stakeholders.
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Dataverse / Data Science Lead (CBP) based in the United States.
We are seeking an experienced, hands-on technical leader to drive advanced analytics and data science initiatives supporting critical federal operations and national security priorities. This role combines expertise in statistical analysis, machine learning, data engineering, and Microsoft Dataverse to turn complex data into reliable, actionable insights. You will lead analytical strategy, assess data quality, develop robust data models, and ensure that analytical findings are accurate, transparent, and decision-ready. Working across multidisciplinary teams, you will establish best practices for data governance, reproducibility, validation, and responsible AI. You will also mentor analysts and data scientists while translating complex technical findings for audiences ranging from technical specialists to senior decision-makers. This opportunity is suited to a strategic thinker who enjoys solving challenging problems and strengthening mission-critical analytical capabilities.
Accountabilities
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Lead analytical strategy for complex operational, workflow, technology, and programmatic questions, aligning methodologies and findings with stakeholder priorities and decision-making needs.
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Serve as the subject-matter expert for Microsoft Dataverse, including data structures, management practices, analytical applications, and integration with reporting, automation, and data science solutions.
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Evaluate data readiness by assessing quality, completeness, sensitivity, lineage, authoritative sources, access requirements, and suitability for intended analytical purposes.
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Design and maintain reporting-ready data models, tables, relationships, transformations, validation rules, business logic, and analytical pipelines.
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Develop, evaluate, and apply statistical models, machine-learning methods, predictive analytics, exploratory analyses, and AI or large language model evaluation approaches.
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Conduct hands-on analysis using Python, SQL, Dataverse queries, and related tools while troubleshooting complex data, integration, transformation, reporting, and modeling challenges.
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Collaborate with operational, program, technical, evaluation, and governance stakeholders to define baselines, performance measures, comparisons, outcomes, and appropriate interpretations of evidence.
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Communicate analytical results through clear, actionable deliverables for technical and nontechnical audiences, explaining uncertainty, assumptions, data-quality issues, potential bias, and causal limitations.
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Lead feature definition, model selection, validation, error analysis, sensitivity testing, bias and fairness assessments, and interpretation for advanced analytics initiatives.
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Establish standards for reproducibility, peer review, documentation, version control, validation, quality assurance, and responsible data and AI use.
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Develop reusable notebooks, SQL scripts, analytical templates, code patterns, Dataverse configuration standards, data dictionaries, and data-quality checks.
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Mentor analysts, data scientists, and technical teams in analytical methods, data management, quality assurance, peer review, and responsible AI practices.
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Coordinate with data engineering, platform, application development, and Power Platform teams on pipeline design, integrations, model handoffs, monitoring, performance, security, and maintainability.
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Lead technical reviews of analytical plans, data models, methods, and results to ensure they are appropriate for the decisions being supported.
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Partner with data owners and governance teams to improve data definitions, provenance, access, quality standards, retention practices, and responsible-use controls.
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Support the development of dashboards, reports, and self-service analytics using approved reporting and visualization tools.
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Stay current with data governance, privacy and security requirements, analytical methodologies, Microsoft Dataverse capabilities, and responsible AI practices.
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Master's degree or higher in statistics, data science, operations research, public health, computer science, engineering, mathematics, or a related quantitative field.
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At least 9 years of progressive experience in applied data science, advanced analytics, statistical analysis, data engineering, or related analytical roles.
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Experience in federal health, healthcare, regulated-data environments, enterprise modernization, or similarly complex, mission-driven organizations.
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Demonstrated ability to lead complex analytical initiatives from problem definition and data assessment through analysis, validation, interpretation, and communication of results.
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Strong hands-on proficiency in Python and SQL, including developing, testing, validating, and troubleshooting analytical workflows, data transformations, statistical models, and machine-learning solutions.
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Experience designing and evaluating data models, pipelines, data-quality controls, validation logic, data lineage, authoritative data sources, and reporting-ready datasets.
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Experience applying one or more advanced analytical methods, such as predictive modeling, time-series analysis, causal inference, natural language processing, machine learning, generative AI, or large language model evaluation.
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Familiarity with modern data, analytics, and visualization platforms, such as Microsoft Dataverse, Databricks, Snowflake, Power BI, Azure, AWS, Google Cloud Platform, or comparable technologies.
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Strong communication skills, with the ability to explain analytical methods, findings, uncertainty, assumptions, bias, data-quality concerns, and limitations to leadership and other stakeholders.
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Experience applying or establishing standards for reproducibility, peer review, documentation, version control, quality assurance, validation, and responsible use of data and AI.
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Demonstrated technical leadership, including mentoring analysts or data scientists, leading peer reviews, developing reusable analytical resources, or managing complex analytical workstreams.
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U.S. citizenship and the ability to obtain a CBP Background Investigation are required; an active CBP Background Investigation is preferred.
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Willingness to travel approximately 20%, including to remote or rugged locations.
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Doctoral degree in statistics, data science, operations research, public health, computer science, engineering, economics, epidemiology, or another related quantitative discipline.
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Experience supporting healthcare, federal health, clinical analytics, public health, operations research, or other fields where data context and interpretation directly influence operational or programmatic decisions.
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Experience deploying analytics or machine-learning solutions through repeatable data pipelines, model monitoring, MLOps practices, model lifecycle management, or collaboration with production engineering and platform teams.
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Experience designing, implementing, or evaluating natural language processing, generative AI, or large language model solutions, including error analysis, evaluation methods, bias and fairness assessments, and responsible-use considerations.
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Hands-on experience with Microsoft Dataverse and the Power Platform, including table design, relationships, security roles, integrations, data management, reporting, and Power BI analytics.
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Advanced certifications in cloud machine learning, data science, analytics, data engineering, Databricks, Azure, AWS, Google Cloud Platform, Power Platform, or comparable technologies.
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Evidence of technical leadership through publications, conference presentations, internal analytical standards, training development, mentoring, peer-review leadership, or enterprise-scale analytical initiatives.
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Target annual salary: $111,209–$180,317.
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Compensation within the stated range depends on factors such as location, internal equity, business considerations, client contract requirements, qualifications, education, experience, skills, and security clearances.
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Work location options include remote work within the United States or Washington, DC.
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Approximately 20% travel is expected, potentially including visits to remote or rugged locations.
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Specific healthcare coverage, retirement plans, paid time off, and other benefits are not detailed in the available job description and should be confirmed during the hiring process.
Requirements:
Preferred Qualifications: