Lead Quantitative Model Solutions Specialist

About this role:

Wells Fargo is seeking a Lead Quantitative Model Solutions Specialist

In this role, you will:

• Lead complex, large-scale model maintenance, optimization, and planning initiatives related to operational processes, controls, reporting, testing, implementation, and documentation
• Review and analyze complex multi-faceted model operations and optimization challenges that require in-depth evaluation of multiple factors including intangibles or unprecedented factors
• Develop model processes and optimization strategies for short- and long-term objectives; support and provide insights regarding a wide array of business initiatives
• Make decisions in complex and multi-faceted situations requiring solid understanding of agile development
• Influence global assessment of model maintenance schedules inclusive of engineering, structure, and scope of review following the System Development Life Cycle process, quality, security, and compliance requirements
• Strategically collaborate and consult with peers, colleagues, and managers to resolve issues and achieve goals

Required Qualifications:

• 5+ years of quantitative model solutions or quantitative model operations experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

• 2+ years of hands-on experience in Python and SQL
• 5+ years of advanced programming and debugging skills in Python OOP, packaging, build and deployment, data structures and algorithms, decorators, logging, exception handling
• 2+ years of experience in High performance computing, Big Data and real time solutions PySpark, MapR streaming, parallel processing, real time optimization.
• 2+ years of experience in unit testing, UAT testing, regression testing and code review
• Comfortable with Git, GitHub, CI/CD pipelines and UNIX commands
• 1+ years of experience in SAS
• Good Written and Oral 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

Job Expectations:

• 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 models for commercial banking
• Identify opportunities and deliver process improvements, standardization, rationalization and automations
• Migration of existing development/implementation codes from SAS to Python with thorough testing and UAT
• Development of regulatory Credit risk (including CCAR, CECL and IFRS), RRP Valuation, and PPNR models for Commercial portfolio in SAS/Python.
• Lead complex model development projects independently and support multiple priorities
• Perform various complex activities related to predictive modeling process enhancements and Python conversions
• Work closely with team to understand and enhance the data and modeling process behind existing models, develop new models, address data and model issues/findings.
• Underlying portfolio data research and analytics using strong programming skills
• Adhere to audit and model validation governance to ensure data and modeling process are in compliance with policy and are working as intended, address model validation and regulatory feedback issues
• Support ad-hoc analytic projects.