Navy Federal Credit Union | Data Scientist I/II (Fair Lending Analytics) | Pensacola, FL | United States | BigDataKB.com | 11/16/2022

Advanced Analytics Focused
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. Understand business needs and identify opportunities for new products, services, and process optimization to meet business objectives through the use of cutting-edge data science. Create descriptive, predictive, and prescriptive models and insights to drive impact across the organization. Conduct work assignments of increasing complexity, under moderate supervision with some latitude for independent judgment. Intermediate professional within field; requires moderate skill set and proficiency in discipline.

Model Risk Management
Provide independent effective challenge to the models developed by the organization’s business units. This includes conducting model validation, statistical analyses, building benchmark/challenger models, assessing model risk, assessing model performance, ensuring documentation completeness, and identifying and presenting findings to various stakeholders within the business units. Conduct work assignments of increasing complexity, under moderate supervision with some latitude for independent judgment. Intermediate professional within field; requires moderate skill set and proficiency in discipline.

Fair Lending Analytics
Provide independent and effective challenge to models, business practices, and policies developed by the organization’s business units to ensure compliance with fair lending laws. This includes statistical analyses to assess fair lending risk, building and assessing alternative models/policies, and identifying and presenting findings to various stakeholders within the business units. Create descriptive, predictive, and prescriptive models and insights to drive impact across the organization. Conduct work assignments of increasing complexity, under moderate supervision with some latitude for independent judgment. Intermediate professional within field; requires moderate skill set and proficiency in discipline.

Fair Lending Analytics Team Overview:
Navy Federal’s Fair Lending Analytics team is an enterprise-wide function that sits within the Compliance and Public Policy division and is responsible for identifying and analyzing gaps and weaknesses in business processes and policies and ensuring that the adequate corrective actions are taken to mitigate the risk. The team primarily conducts statistical analysis for identifying and evaluating potential fair lending risk across the credit lifecycle. The team is responsible for fair lending testing of Navy Federal models, and we apply statistically techniques to ensure models are fair.

This role will aid the Fair Lending Analytics team in its analysis, modeling, and reporting efforts across various product areas. We are seeking candidates with great data and communication skills and an interest in consumer protection for this position.

Credit Policy & Analytics
Provide analytical insights and perform analysis to assist in guiding credit risk decisions and business planning. Participate in a variety of analytical efforts to support automation, model development, business analysis, and reporting. Deliver insights to auditors, regulators, rating agencies, model risk management, and other key decision makers. Conduct work assignments of increasing complexity under moderate supervision with some latitude for independent judgment. Intermediate professional within field.

Marketing & Communications
Develop models and analytics to better understand impact of NFCU Brand on prospects, members and their engagement with NFCU. Develop models and analytics to optimize targeting strategies incorporating segmentation and channel interaction and other analytics to drive more personalized experience for members. Expand upon the use of Machine Learning techniques using Member’s cross channel interaction data. Build models and integrate results with business data to understand how channel behavior affects product uptake and engagement. Perform qualitative and quantitative analysis on channel interactions to improve channel experience, engagement, and usage.

Responsibilities

• Support the delivery of strategic advanced analytics solutions across the organization with solutions drawing on descriptive, predictive, and prescriptive analytics and modeling
• Leverage a broad set of modern technologies – including Python, R, Scala, and Spark – to analyze and gain insights within large data sets
• Manage, architect, and analyze big data in order to build data driven insights and high impact data models
• Evaluate model design and performance and perform champion/challenger development. Analyze model input data, assumptions, and overall methodology.
• Using statistical practices, analyze current and historical data to make predictions, identify risks, and opportunities, enabling better decisions on planned/future events
• Provide analytics insights and solutions to solve complex business problems
• Apply business knowledge and advanced statistical modeling techniques when building data structures and tools
• Collaborate with other team members, subject matter experts, pods, and delivery teams to deliver strategic advanced analytic based solutions from design to deployment
• Examine data from multiple sources and share insights with leadership and stakeholders
• Transform data presented in models, charts, and tables into a format that is useful to the business and aids in effective decision making
• Point of contact between the data analyst/data engineer and the project/functional analytics leads
• Develop and maintain an understanding of relevant industry standards, best practices, business processes and technology used in modeling and within the financial services industry
• Identify improvements to the way in which analytics service the entire function
• Recognize potential issues and risks during the analytics project implementation and suggest mitigation strategies
• Prepare project deliverables that are valued by the business and present them in such a manner that they are easily understood by project stakeholders
• Perform other duties as assigned
• Conduct model validations, assess model performance, and evaluate model conceptual soundness, including input data, assumptions, statistical and analytical testing, and general development methodology
• Conduct model outcomes analysis, including back-testing
• Assess new and existing model’s overall fit/suitability with its intended use and purpose
• Provide subject matter expertise to identify models’ key assumptions, limitations and weaknesses, and recommend practical solutions to mitigate model risk
• Develop benchmark/challenger models to assess strength of model under review
• Prepare and present clear, thorough reports to model developers and model owners explaining the analysis performed, results of the analysis and recommendations for improvement
• Perform other duties as assigned
• Evaluate fair lending risks associated with models and policies by reviewing input data, assumptions, and by conducting statistical and analytical testing
• Provide subject matter expertise to recommend practical solutions to mitigate fair lending risk
• Develop alternative models as necessary
• Leverage understanding of Artificial Intelligence (AI) and Machine Learning (ML) techniques
• Prepare and present clear, thorough reports to business units and executives explaining the analysis performed, results of the analysis and recommendations for improvement
• Conduct statistical analysis to assess the fair lending risk across multiple business unit processes and policies including but not limited to underwriting, pricing, redlining, and loss mitigation; and provide guidance and advice to business units based on the results of the analysis
• Create data visualizations and/or dashboards to monitor and explain risk trends that are relevant for loss and originations modeling
• Conduct model validations and routinely assess model performance
• Create reports and other deliverables to assist with business planning, continuity, and strategy
• Lead initiatives to streamline or automate processes related to data preparation, quality assurance, execution of in-production models, report creation
• Use a variety of analytical and modeling techniques to develop and/or refine strategies related to underwriting criteria, pricing, line management, loss forecasting, loan loss reserves to drive business direction across multiple asset classes (e.g., Auto, Unsecured, Cards, Mortgage, etc.)
• Conduct model outcomes analysis, including back-testing
• Assess new and existing model’s overall fit/suitability with its intended use and purpose
• Provide subject matter expertise to identify models’ key assumptions, limitations/weaknesses, and recommend practical solutions to mitigate model risk
• Develop benchmark/challenger models to assess strength of model under review
• Prepare and present clear, thorough reports to model developers and model owners explaining the analysis performed, results of the analysis and recommendations for improvement

Qualifications

• Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering or another quantitative field, or related field, or the equivalent combination of education, training and experience
• Ability to understand complex business problems and determine what aspects require optimization and articulate those aspects in a clear and concise manner
• Advanced skill in communicating actionable insights using data to technical and non-technical audiences
• Proven experience working in a dynamic, research-oriented groups with several ongoing concurrent projects
• Demonstrates functional knowledge of data visualization libraries such as matplotlib or ggplot2; knowledge of other visualization tools such as Microsoft Power BI and Tableau
• Ability to manipulate raw data within visualization tools to create effective dashboards that communicate end-to-end data outcomes visually
• Proficient in storytelling with data skills
• Exceptional technical writing skills
• Advanced skill communicating thoughts, concepts, practices effectively at all levels, adjusting as needed to a target audience
• Advanced verbal, interpersonal and written communication skills
• Advanced database, word processing, spreadsheet, and presentation software skills (e.g., Microsoft Access, Excel, PowerPoint, etc.)
• Moderate skill in descriptive, predictive, and prescriptive analytics and modeling; demonstrated success in building models that are deployed and have made measurable business impact
• Experience in using two or more of the following modeling types to solve business problems: classification, regression, time series, clustering, text analytics, survival, association, optimization, reinforcement learning
• Working knowledge of advanced techniques such as: SMOTE, dimension reduction techniques, natural language processing, sentiment analysis, anomaly detection, geospatial analytics, etc.
• Demonstrates a deep understanding of the modeling lifecycle
• Moderate skill data mining, data wrangling, and data transformation with both structured and unstructured data; deep understanding of data models
• Skill interpreting, extrapolating, and interpolating data for statistical research and modeling
• Advanced skill in Data Interpretation, Qualitative and Quantitative Analysis
• Advanced skill in Python, R, and/or Scala
• Moderate skill in SQL and querying (able to pull/transform your own data)
• Knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure ML (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud
• Understanding of data models, large datasets, business/technical requirements, BI tools, statistical programming languages and libraries
• Familiar with Data Engineering concepts
• Familiar with the use of standard ETL tools and techniques
• Familiar with the concepts and application of data mapping and building requirements
• Demonstrates a deep understanding of multiple data related concepts
• Familiar with Data Integration, Data Governance and Data Warehousing
• Moderate skill in Data Management, Data Validation & Cleansing and Information Analysis
• Proficient in R or Python or Spark to analyze large data sets and develop predictive models
• Proficient in R, Python, SQL, or SAS
• Familiarity with federal fair lending laws, such as the Fair Housing Act (FHA) and Equal Credit Opportunity Act (ECOA)
• Experience with model reviews in newly emerging machine learning/artificial intelligence (ML/AI) techniques applied to big data such as gradient boosting machines, support vector machines, and random forests is a plus
• Familiarity with testing for bias in ML/AI is a plus.
• Familiar with ArcGIS or other mapping tools is a plus
• Working knowledge of accounting standards as they relate to Allowance of Loan and Lease Losses and Current Expected Credit Loss (CECL) standard

Desired

• Doctoral degree in Statistics, Mathematics, Computer Science, Engineering or another quantitative field, or related field
• Working knowledge of Navy Federal Credit Union instructions, standards, and procedures
• Familiar with project management concepts and frameworks such as Agile Frameworks (SAFE), Communication Strategy and Management, Delivery Excellence and Requirements management

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

Location: 820 Follin Lane, Vienna, VA 22180 | 5550 Heritage Oaks Dr. Pensacola, FL 32526 | 141 Security Dr. Winchester, VA 22602

Navy Federal is now hybrid! Our standard enterprise requirement for a hybrid schedule is to report on-site 4-16 days each month. The number of days reporting on-site will ultimately be determined by the employee’s leadership and business unit needs. You will learn more throughout the hiring and on boarding process.

Salary Range: $83,100 – $156,100annually (Level I); $95,600 – $179,700 annually (Level II)

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.