About this role:

Wells Fargo is seeking a Senior Quantitative Analytics Specialist.

The Commercial Credit Decision Modeling team is responsible for model development, strategy, implementation, and data management for all Commercial Lending portfolios.

In this role, you will:

• Perform highly complex activities related to creation, implementation, and documentation
• Use highly complex statistical theory to quantify, analyze and manage markets
• Forecast losses and compute capital requirements providing insights, regarding a wide array of business initiatives
• Utilize structured securities and provide expertise on theory and mathematics behind the data
• Manage market, credit, and operational risks to forecast losses and compute capital requirements
• Participate in the discussion related to analytical strategies, modeling and forecasting methods
• Identify structure to influence global assessments, inclusive of technical, audit and market perspectives
• Collaborate and consult with regulators, auditors and individuals that are technically oriented and have excellent communication skills

• 4+ 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 a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science

• Ph.D. in economics, statistics, finance, math, engineering or similar quantitative disciplines
• 4+ years of experience in Credit Risk Model development or Credit Risk strategies
• Exposure to machine learning techniques (Random Forest, XG Boost, Light GBM, Neural Networks, and so on)
• Developed Credit and/or Fraud 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
• Develop best-in-class credit scorecard models leveraging cutting-edge advanced AI/ML techniques
• Analyze and explore predictive patterns using big data translating into enhanced strategies/rules leading to minimized fraud loss
• 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
• 4+ 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
• 4+ 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.