Overview

The Credit Risk & Decision Science team within the Lending Analytics department is responsible for developing and implementing analytical solutions that drive changes to underwriting policies across Navy Federal Credit Union's various lending products. Data scientists within this team will focus on two areas. Firstly, they will leverage analytics to define and monitor credit card account management strategies such as proactive line increase and reactive line increase to name a few. This will include developing and recommending test and control strategies to better understand cardholder behavior. Secondly, data scientists will leverage the existing pricing framework to support rate changes across various lending portfolios. Successful candidates will exhibit effective communication and analytical skills as well as strong leadership qualities.

• Design, develop, and evaluate basic/routine predictive models and algorithms with some complexity
• Analyze and interpret results with some complexity
• Limited judgment and discretion within defined procedures and practices
• Develop and code basic software programs, algorithms, and automated processes
• Use modeling and trend analysis to analyze data
• Collaborate with team members and participate in team projects and initiatives
• Utilize effective written and verbal communication to document and present findings

• 3-5 years of experience in exploratory data analysis
• Manage, architect, and analyze big data to build data driven insights and high impact models
• Evaluate model design and performance and perform champion/challenger development. Analyze model input data, assumptions, and overall methodology
• SQL, R, Python, Hadoop, SAS, SPSS, Scala, AWS, Databricks
• Research Skills
• Strong written and verbal communicator to technical and non-technical audiences
• Collaborate with other team members, subject matter experts, pods, and delivery teams to deliver strategic advanced analytic based solutions from design to deployment
• Bachelor's Degree in Data Science, Statistics, Mathematics, Computers Science, Engineering, or degrees in similar quantitative fields

• Master's Degree in Data Science, Statistics, Mathematics, Computer Science, or Engineering
• Familiar with analytical frameworks used to support the pricing of lending products
• Familiar with analytical models/analysis used to support credit card underwriting and account management underwriting policies
• Familiar using GitHub for documentation and code collaboration purposes