Job Description

Develop and implement statistical models to detect and prevent fraud using behavioral, financial, and organizational data. Create descriptive and predictive models, analyze complex datasets for patterns, and collaborate with cross-functional teams to enhance fraud detection systems. Stay updated on emerging industry threats, ensure data integrity and model risk compliance, and communicate analytical insights to stakeholders and executive leadership.

Navy Federal provides much more than a job. We provide a meaningful career experience, including a culture that is energized, engaged and committed; and fierce appreciation for our teams, who are rewarded with highly competitive pay and generous benefits and perks.

Our approach to careers is simple yet powerful: Make our mission your passion.

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Equal Employment Opportunity: All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected Veteran.

Hybrid Workplace: Navy Federal Credit Union is a hybrid workplace, and details will be discussed during your interview process.

Disclaimers: Navy Federal reserves the right to fill this role at a higher/lower grade level based on business need. An assessment may be required to compete for this position. Job postings are subject to close early or extend out longer than the anticipated closing date at the hiring team’s discretion based on qualified applicant volume. Navy Federal Credit Union assesses market data to establish salary ranges that enable us to remain competitive. You are paid within the salary range, based on your experience, location and market position.

Bank Secrecy Act: Remains cognizant of and adheres to Navy Federal policies and procedures, and regulations pertaining to the Bank Secrecy Act.

• 3-5 years of experience in exploratory data analysis 
• Experience working with highly imbalanced datasets and applying techniques such as resampling, cost-sensitive learning, or anomaly detection
• Ability to communicate complex technical findings to non-technical stakeholders
• Basic understanding of business and operating environment
• Experience with coding in SQL, R, Python
• Model lifecycle execution
• Technical writing
• Data storytelling and technical presentation skills
• Working knowledge of procedures, instructions, and validation techniques
• Prior experience in financial services, payments, or fintech is a plus
• Bachelor's Degree in Data Science, Statistics, Mathematics, Computers Science, Engineering, or degrees in similar quantitative fields

Desired Qualification

• Graduate Degree in Data Science, Statistics, Mathematics, Computers Science, or Engineering

Hours: Monday - Friday, 8:00AM - 4:30PM

Location: 820 Follin Lane, Vienna, VA 22180 

• Develop machine learning models to identify fraudulent activities, minimize financial losses and enhance member experience
• Conduct deep-dive analyses on fraud patterns
• Work with large-scale datasets, ensuring data quality, feature engineering and model interpretability
• Conduct A/B testing, analyze and interpret results, and monitor model performance 
• Develop and code moderately complex software programs, algorithms, and automated processes
• Use modeling and trend analysis to analyze data and provide insights
• Partner with other teams to translate business needs into data-driven solutions
• Transform data into charts, tables, or other data visualizations that aid effective decision making
• Build working relationships with team members and subject matter experts
• Lead small projects and initiatives
• Utilize effective written and verbal communication to document and present findings of analyses to a diverse audience of stakeholders
• Ensure compliance through model life cycle and model risk management documentation