Principal Data Scientist at Fa Etbx Saasfaprod1 — Vienna

About The Role

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.

Developing and deploying NFCU Lending CECL and credit risk predictive models, post-model adjustment, advanced analytics, and advanced statistical techniques, solutions and actionable insights that deliver business impacts. Provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission critical decision making for various areas of the organization. Create descriptive, predictive, and prescriptive models and insights to drive impact across the organization. Regarded as an advanced professional in the data science field. Conduct complex work under minimal supervision and with wide latitude for independent judgment. Mentor lower level staff.

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Contribute to the end-to-end CECL model development, implementation, execution, monitoring, documentation, and governance for consumer lending portfolios
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
Utilize effective written and verbal communication to document analyses and present findings analyses to a diverse audience of stakeholders
Develop and maintain strong working relationships with team members, subject matter experts, and leaders
Lead moderate to large projects and initiatives
Model best practices and ethical AI
Works with senior management on complex issues
Assist with the development and enhancement practices, procedures, and instructions
Serve as technical resource for other team members
Mentor the junior team members, providing guidance on both credit risk forecasting and financial performance assessments to develop talent and enhance organizational capabilities.
6+ 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
Python, R, SAS, SQL, Hadoop, SPSS, Scala, AWS
Model 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/PhD Degree in Data Science, Economics, Statistics, Mathematics, Computers Science, or Engineering
Advanced knowledge of CECL reserving, credit loss forecasting, and Model Risk Management guidelines.
Experience with consumer lending portfolios such as credit card, auto, secured consumer lending, unsecured consumer lending, mortgage, or home equity.
Familiarity with loan-level or account-level credit loss modeling techniques, including probability of default, loss given default, exposure at default, prepayment, survival/hazard, and competing risk models.
Experience implementing controlled model production processes, including version control, data validation, reconciliation, monitoring, documentation, and change management.
Experience responding to Model Risk Management validation, internal audit, external audit, regulatory, accounting, or control review questions.
Advanced knowledge of applicable federal and state laws, rules, and regulations that govern credit card, secured consumer lending, and unsecured consumer lending.
Advanced knowledge of banking and financial industry trends, products, services, credit cycles, portfolio performance drivers, and reserve implications.