Overview Nakpuna Prime is looking for a Senior Data Scientist to deliver evidence-based insights through advanced statistical analysis and predictive modeling to support student success and strategic enrollment initiatives. This senior role leads analytical approach, modeling standards, and stakeholder engagement, and mentors data scientists to deliver high-impact insights and predictive products. This effort supports the United States Naval Community College (USNCC). Responsibilities The following reflects management’s definition of essential functions for this job but does not restrict the tasks that may be assigned. Management may assign additional duties and responsibilities to this job at any time due to reasonable accommodation or other reasons. Design, train, and validate statistical and machine learning models to forecast student outcomes and identify students needing intervention. Conduct advanced analysis (e.g., cohort analysis, A/B testing, causal inference) to identify trends and correlations across student datasets. Identify and present actionable student success insights supported by rigorous statistical methods. Develop and document student risk classification logic (high/medium/at-risk) using composite scoring from grades, attendance, and LMS activity. Collaborate with Data Engineers and BI Developers to operationalize models into dashboards and recurring deliverables. Maintain clear model documentation for reproducibility, auditing, and governance. Lead model and analysis design reviews, establishing standards for evaluation, documentation, and reproducibility. Mentor data scientists and analysts; provide guidance on methodology, prioritization, and stakeholder communication. Partner with engineering and BI leads to operationalize predictive outputs into dashboards and recurring deliverables. Qualifications Qualification/Skills : Expertise in advanced statistical analysis, modeling, and interpretation, including governance-ready documentation of methods. Proven ability to develop, validate, and maintain predictive models end-to-end, including monitoring and recalibration strategies. Strong stakeholder engagement skills to translate analytical outputs into decision-ready recommendations and narratives. Experience leading analytical standards (feature definitions, model evaluation, bias/robustness checks) and peer review. Leadership skills to mentor data scientists and coordinate cross-functionally to operationalize analytics into products. Education and Experience: This effort supports the United States Naval Community College (USNCC).: This position requires a bachelor’s degree or higher in Data Science, Statistics, or a related quantitative field. The following are desirable levels of experience: 10+ years of experience applying advanced analytics and predictive modeling to large datasets Demonstrated expertise in statistical methods, experimental design, and machine learning lifecycle management Experience designing risk classification approaches and forecasting models aligned to stakeholder decisions Experience operationalizing models into dashboards and decision workflows in partnership with engineering/BI Demonstrated experience mentoring analysts/scientists and communicating results to executive stakeholders Military Installation Access and Security Clearance: This position requires access to military installations. Must be able to qualify for and obtain base access and pass a background check. Must be a U.S. citizen. Location: This position is hybrid. Work will be done on site at least three (3) days a week in Arlington, VA. Remote candidates will be considered. Occasional travel may be required. Physical Requirements: The ideal candidate must at a minimum be able to meet the following physical requirements of the job with or without a reasonable accommodation: Ability to perform repetitive motions with the hands, wrists, and fingers. Ability to engage in and follow audible communications in emergency situations. Ability to sit for prolonged periods at a desk and working on a computer. Ability to walk for extended periods throughout the work day including moving between buildings and covering long distances by foot.