About this role:

Wells Fargo is seeking a Quantitative Analytics Specialist.

Wells Fargo enables global talent capabilities for Wells Fargo Bank NA., by supporting business lines and staff functions across Technology, Operations, Risk, Audit, Process Excellence, Automation and Product, Analytics and Modeling.

The Retail Credit Risk Modeling team is responsible for development and monitoring of Retail Credit Risk models across all portfolios

In this role, you will:

• Develop, implement, and calibrate various analytical models
• Perform highly complex activities related to financial products, business analysis and modeling
• Perform basic statistical and mathematical models using Python, R, SAS, C++ and SQL
• Perform analytical support and provide insights regarding a wide array of business initiatives
• Provide solutions to business needs and analyze workflow processes to make recommendations for process improvement in risk management
• Collaborate and consult with peers, colleagues, managers, and regulators to resolve issues and achieve goals

• 2+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• Master's degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline

• 2+ years of scorecard development, credit or fraud risk analytics experience, or a combination of both.
• Master's degree or higher in a quantitative field such as mathematics, statistics, engineering, physics, economics, or computer science
• 2+ years of programming experience in SAS, Python or R
• Experience in credit and/or fraud risk model development and/or monitoring
• Excellent verbal and written communication skills
• Experience in producing high quality technical documentation with tools such as Excel, Word, PowerPoint.
• Develop best-in-class transactional and application credit risk models leveraging cutting-edge advanced AI/ML techniques.
• Analyze and explore predictive patterns using big data translating into enhanced strategies/rules
• Use Python to build end to end Machine Learning models starting from data processing to feature engineering to model estimation.
• Lead and participate in critical debugging, testing and performance tuning for machine learning and/or statistical models written in python code
• Conduct ad-hoc analysis and reporting as required
• Responsible for documenting and presenting detailed model development processes and results, suitable for a variety of audiences
• Lead and participate intensive team discussions, interactions with cross-functional teams, and dialogues with internal reviewers (Model Validation and Internal Audit)
• Collaborate with key business model users to ensure models are business driven, properly implemented and executed
• Respond to ongoing analytical requests from auditors and regulatory reviewers in timely manner.
• Ph.D. in economics, statistics, finance, math, engineering or similar quantitative disciplines
• 2+ years of experience in Credit Risk Model development
• Exposure to machine learning techniques (Random Forest, XG Boost, Light GBM, Neural Networks, and so on)
• Developed Credit Risk Model using Python
• Familiarity with Credit Bureau Data
• Excellent problem-solving skills and ability to connect dots, see big picture and find solutions and articulate in a clear manner.
• Understanding of process, methodologies used in credit/fraud scoring model development, implementation, validation and monitoring
• Ability to effectively manage multiple assignments with challenging timelines
• Experience in statistical modeling techniques.