LEAD QUANTITATIVE ANALYTICS SPECIALIST

• Company Wells Fargo
• No. of Openings 4
• Salary Not Disclosed
• Work Type on-site

Job Description :

About this role:

Wells Fargo is seeking a Lead Quantitative Analytics Specialist...

In this role, you will:

• Lead complex initiatives including creation, implementation, documentation, validation, articulation and defense, of highly statistical theory
• Qualify monitor markets and forecast credit and operational risks
• Strategize short and long-term objectives, and provide analytical support for a wide array of business initiatives
• Utilize stochastic, structured securities, spread analysis, with the expertise in the theory and mathematics behind the analysis
• Review and assess models inclusive of technical, audit, and market perspectives
• Identify structure and scope of review
• Enable decision making for product and marketing with broad impact and act as key participant to develop and document analytical models
• Collaborate and consult with regulators and auditors
• Present results of analysis and strategies

Required Qualifications:

• Over all experience around 8 to 12 years in similar role
• Bachelor’s degree or higher in a quantitative fields such as Computer Science, Applied mathematics, engineering, statistics, finance or econometrics from top tier institutes
• Strong problem solving skills
• 8+ years of experience in credit risk analytics with exposure to statistical and machine learning model development, implementation or ML Ops
• 2+ years of advanced programming expertise in SAS
• 5+ years of advanced programming and debugging skills in Python – OOP, packages & modules, data structures and algorithms, decorators, logging, exception handling, JIT compilers
• 2+ years of experience in High performance computing and Big Data – PySpark, MapR streaming, parallel processing.
• Comfortable with Git, GitHub, CI/CD pipelines and UNIX commands
• Strong project management skills with ability to prioritize work, meet deadlines, achieve goals, and work under pressure in a dynamic and complex environment
• Excellent verbal, written, and interpersonal communication skills
• Strong ability to develop partnerships and collaborate with other business and functional areas
• Knowledge and understanding of issues or change management processes
• Experience determining root cause analysis
• Flexibility with changing priorities

Desired Qualifications:

• Ability to prioritize work, meet deadlines, achieve goals and work under pressure in a dynamic and complex environment
• Detail oriented, results driven, and has the ability to navigate in a quickly changing and high demand environment while balancing multiple priorities
• Understanding of bank regulatory data sets and other industry data sources
• Ability to research and report on a variety of issues using problem solving skills
• Ability to make timely and independent judgment decisions while working in a fast-paced and results-driven environment

Job Expectations:

• Perform various complex activities related to predictive modeling process enhancements and Python conversions.
• Advanced Python based Implementation, execution and monitoring of credit ACL, CECL, IFRS9, Basel and CCAR models for Home Lending, Auto, Unsecured (Cards, Loans, Small Business Loans, Other) portfolios and commercial portfolios
• Support python big data implementation of Balance Sheet Modeling with focus on balance forecasting, loan spread modeling and deposit modeling & Fee Income and Expense Modeling with focus on non-interest income and expense related models
• Provide engineering and analytical solutions across model development, implementation, monitoring and production in a Big Data environment
• Support implementation of python based solutions for real time and/or batch based Machine Learning scorecard models for consumer and commercial banking
• Identify opportunities and deliver process improvements, standardization, rationalization and automations