Dataverse / Data Science Lead (CBP)

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.

• Master's degree or higher in statistics, data science, operations research, public health, computer science, engineering, mathematics, or a related quantitative field.

• 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.

• Demonstrated ability to lead complex analytical initiatives from problem definition and data assessment through analysis, validation, interpretation, and communication of results.

• Strong hands-on proficiency in Python and SQL, including developing, testing, validating, and troubleshooting analytical workflows, data transformations, statistical models, and machine-learning solutions.

• Experience designing and evaluating data models, pipelines, data-quality controls, validation logic, data lineage, authoritative data sources, and reporting-ready datasets.

• 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.

• Familiarity with modern data, analytics, and visualization platforms, such as Microsoft Dataverse, Databricks, Snowflake, Power BI, Azure, AWS, Google Cloud Platform, or comparable technologies.

• Strong communication skills, with the ability to explain analytical methods, findings, uncertainty, assumptions, bias, data-quality concerns, and limitations to leadership and other stakeholders.

• Experience applying or establishing standards for reproducibility, peer review, documentation, version control, quality assurance, validation, and responsible use of data and AI.

• Demonstrated technical leadership, including mentoring analysts or data scientists, leading peer reviews, developing reusable analytical resources, or managing complex analytical workstreams.

• • Doctoral degree in statistics, data science, operations research, public health, computer science, engineering, economics, epidemiology, or another related quantitative discipline.

  • 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.

  • 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.

  • 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.

  • Hands-on experience with Microsoft Dataverse and the 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 technical leadership through publications, conference presentations, internal analytical standards, training development, mentoring, peer-review leadership, or enterprise-scale analytical initiatives.