Senior Data Scientist (Fair Lending Analytics)

Overview

Navy Federal Credit Union currently does not provide sponsorship for this role. Applicants must be authorized to work in the United States without the need for current or future sponsorship.

Provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support missioncritical decision making across the organization. Understand business needs and identify opportunities for new products, services, and process optimization through the use of advanced data science techniques.

This role supports risk and compliancefocused analytics, including the evaluation of models, policies, and business practices for potential fair lending and consumer compliance risk. The Senior Data Scientist will conduct descriptive, predictive, and prescriptive analyses to assess outcomes, test assumptions, and identify potential bias, while partnering with stakeholders to support responsible and compliant business decisions.

Conduct work assignments of increasing complexity under moderate supervision with latitude for independent judgment. Intermediate professional within field; requires a moderate to advanced skill set and proficiency in data science and analytics.

Responsibilities

• Design, develop, and evaluate moderately complex predictive models and advanced algorithms

• Identify meaningful insights from large, complex data and metadata sources

• Test hypotheses and models; analyze, interpret, and document results

• Review input data, assumptions, and outcomes of models and analytical frameworks to assess potential risk and fairness considerations

• Apply statistical analysis, modeling, and trend analysis to evaluate outcomes and provide actionable insights

• Develop and code moderately complex software programs, algorithms, and automated analytical processes

• Create data visualizations, dashboards, charts, and tables that support effective decisionmaking and risk monitoring

• Develop understanding of best practices related to model governance, ethical AI, and responsible use of analytics

• Collaborate with business partners, compliance, legal, and subjectmatter experts to support analysis and risk mitigation

• Provide clear written and verbal communication of analytical findings to technical and nontechnical stakeholders, including leadership

• Lead or contribute to small projects and team initiatives

Qualifications

• 3-5 years of experience in exploratory data analysis, statistics, or related analytical work
• Bachelor's Degree in Data Science, Statistics, Mathematics, Computers Science, Engineering, or degrees in similar quantitative fields
• Programming, data modeling, simulation, and applied mathematics skills
• Proficiency in SQL, R, Python, SAS, or similar statistical programming languages
• Experience with model development and lifecycle execution
• Technical writing and data storytelling skills, with the ability to explain statistical concepts to diverse audiences
• Familiarity with data visualization and reporting tools (e.g., Tableau, Power BI)
• Working knowledge of procedures, instructions, and validation techniques
• Ability to collaborate effectively and build working relationships across teams
• Exercise sound judgment and discretion within defined procedures and practices
• Problem Solving (Responds as problems and issues are identified)

Desired Qualifications

• Master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or related field
• Experience supporting compliance, risk, or fair lending analytics in a financial services environment
• Familiarity with fair lending or consumer protection laws (e.g., ECOA, FHA) and evaluating models or policies for potential bias
• Experience assessing or validating models, including machine learning models, for fairness or governance considerations

Hours: Monday - Friday, 8:00AM - 4:30PM
Location: 820 Follin Lane, Vienna, VA 22180 | 5510 Heritage Oaks Drive, Pensacola, FL 32526 | 141 Security Drive, Winchester, VA 22602

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