Principal Data Scientist; Credit Risk Small Business and Commercial
Job in
Vienna, Wood County, West Virginia, 22184, USA
Listed on 2026-10-17
Listing for:
Navy Federal Credit Union
Full Time
position Listed on 2026-10-17
Job specializations:
-
IT/Tech
Data Scientist, Data Analyst, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Job-:
Data Scientist
Location:
Vienna, VA, United States Workplace:
Hybrid
Schedule:
Full time, Regular Hours Posted:
Closes:
Targeted Salary Range: $128,500 - $164,700 annually
Provide strategic data science, machine learning, and advanced analytics leadership to inform mission-critical decisions across the organization, with a primary focus on small business lending. Leverage small business credit, application, financial statement, cash flow, transaction, and performance data to shape underwriting, risk segmentation, pricing, portfolio management, and the end-to-end member lending experience. Partner with senior leaders and cross-functional stakeholders to identify high-impact opportunities, influence product and credit strategies, and translate complex data into scalable analytical solutions, actionable insights, and measurable business outcomes.
Advance the organization’s analytical capabilities through descriptive, predictive, and prescriptive modeling and the responsible application of leading data science techniques. Operate as a trusted advisor and advanced practitioner, leading complex initiatives with broad independent judgment and developing analytical talent across the team. Qualifications6+ years of experience with requisite competencies
Complete knowledge and full understanding of specialization
Statistics, machine learning , data mining, data auditing, aggregation, reconciliation, and visualization
Programming, data modeling, simulation, and advanced mathematics SQL, R, Python, Hadoop, SAS, SPSS, Scala, AWSModel lifecycle execution
Technical writing
Data storytelling and technical presentation skills
Research Skills Interpersonal Skills Advanced knowledge of procedures, instructions and validation techniques
Model Development Communication Critical Thinking Collaborate and Build Relationships Initiative with sound judgement
Technical (Big Data Analysis, Coding, Project Management, Technical Writing, etc.)Independent Judgment Problem Solving (Identifies the constraints and risks)
Bachelor's Degree in Data Science, Statistics, Mathematics, Computers Science, Engineering, or degrees in similar quantitative fields
Desired Qualifications Master’s or doctoral degree in data science, statistics, mathematics, computer science, engineering, or a related quantitative discipline
Demonstrated success applying data science, machine learning, or advanced analytics to small business banking, commercial lending, or small business lending strategies
Deep experience designing and implementing analytical solutions across the lending lifecycle, including originations, underwriting, credit decisioning, pricing, account management, portfolio monitoring, and collections
Strong command of small business credit risk, cash flow underwriting, commercial credit data, financial statements, transaction data, and alternative data sources
Proven ability to influence senior leaders and partner across lending product, credit risk, underwriting, operations, and model risk to translate analytical insights into strategic business decisions Additional Information
Hours:
Monday - Friday, 8:00AM - 4:30
PM
Location:
820 Follin Lane, Vienna, VA 22180
Responsibilities Design, develop, and evaluate large and complex predictive models and advanced algorithms Test hypotheses/models, analyze, and interpret results
Develop actionable insights and recommendations
Develop and code complex software programs, algorithms, and automated processes
Use evaluation, judgment, and interpretation to select right course of action
Work on problems of diverse scope where analysis of information requires evaluation of identifiable factors
Produce innovative solutions driven by exploratory data analysis from complex and high-dimensional datasets
Transform…
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