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Manager II, Data Science; CECL Modeling

Job in Vienna, Fairfax County, Virginia, 22184, USA
Listing for: Navy Federal Credit Union
Full Time position
Listed on 2026-06-10
Job specializations:
  • IT/Tech
    Data Science Manager, Data Analyst
Job Description & How to Apply Below
Position: Manager II, Data Science (CECL Modeling)
Overview

Responsible for developing and deploying CECL predictive models, advanced analytics, and advanced statistical techniques, solutions and actionable insights that deliver business impact. Develops strategic priorities, shapes analytical approaches, ensures model quality and performance and ensures alignment with corporate initiatives and goals. Manage daily activities of professional employees. Work is performed under limited supervision.

This position is eligible for the Talent Quest employee referral program. If an employee referred you for this job, please apply using the system-generated link that was sent to you.

Responsibilities
  • Manage, lead and mentor teams of data scientists and analysts in developing advanced statistical models, algorithms, and data mining techniques translating business problems into advanced modeling approaches in support business initiatives
  • Lead end-to-end CECL model development, implementation, execution, monitoring, documentation, and governance for consumer lending portfolios.
  • Drive the evolution of our data science practices by implementing best practices in model development, validation, and deployment across various business units
  • Manage and lead modeling function to ensure compliance with CECL, and collaborate with control team on on-going CECL Modeling Program and controls around the CECL reserve; ensure mathematical and conceptual soundness of all models and data integrity
  • Collaborate with Finance/Accounting and other business partners to establish model requirements and align model techniques to Generally Accepted Accounting Principles
  • Translate portfolio risk dynamics, macroeconomic conditions, model outputs, reserve drivers, and model limitations into clear executive-level insights and recommendations.
  • Communicate complex data-driven insights in a clear and concise manner to diverse audiences, fostering understanding and buy-in from stakeholders at all levels
  • Manage multiple teams and/or specialized units to include resource planning, ensuring the execution of complex modeling initiatives; partner with leaders in analytics, engineering, and business domains to integrate models into workflows
  • Analyze large complex datasets to extract actionable insights and present findings to stakeholders, using effective visualization and storytelling techniques
  • Review complex code and analyses to ensure accuracy, scalability, and ethical alignment
  • Ensure the continuous improvement of data quality and governance processes by collaborating with data engineering and IT teams
  • Ensure appropriate use of advanced statistical techniques and manage model lifecycle governance
  • Develop and manage budgets related to initiatives, ensuring effective resource utilization and alignment with organizational goals
  • Coach teams on cutting-edge data science methods and foster a culture of experimentation and curiosity
  • Research, evaluate, and integrate new data science technologies and methodologies to enhance the team's capabilities and drive innovation, while ensuring transparency, explainability, validation readiness, and compliance with CECL and model risk management expectations.
Qualifications
  • Bachelor's degree in related field or equivalent combination of training, education and experience
  • College/university degree and 7+ years work experience in credit risk modeling, loss forecasting, reserve modeling, financial services analytics, or related quantitative discipline
  • 3+ years of people leadership experience managing quantitative analysts, data scientists, or model developers.
  • Strong ability to create an environment that emphasizes data-driven decision-making across an organization, empowering teams to leverage analytical insights effectively
  • In-depth knowledge of data science tools and technologies (e.g., Python, R, SQL, SAS) to guide team workflows and methodologies
  • Strong presentation and storytelling skills to distill complex models and predictions into understandable insights for diverse audiences, including non-technical stakeholders
  • Skill in establishing clear metrics and performance indicators to monitor the effectiveness of data science projects and ensure alignment with business outcomes
  • St…
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