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.
