About this role:

Wells Fargo is seeking a Lead Quantitative Analytics Specialist for Model Risk Management Team.

MRM operates in a fast-paced work environment with continuously changing policies and technologies. The successful candidate as a Lead Quantitative Analytics Specialist is expected to be self- motivated, require minimal supervision, and produce model validation work that is consistent with CMoR's recognized high standards. Effective model validation work will involve familiarity with source systems of record, analytical data and sampling plans, model replications, model performance assessments, and test model development as effective challenges, benchmarking, stress testing, sensitivity analysis of Deposit models, including deposit balance, rate, and attrition forecasting, as well as depositor's behavior modeling, ALM (Asset liability management), interest rate models. It further requires strength in writing detailed standard analytical reports to ensure Wells Fargo's compliance with governance policies and regulations. Each validation report will include assessments of the specific business, model purpose and history, the model methodology, data integrity, model development, performance, implementation, and monitoring. These documents are read by a broad audience, including auditors and regulators

• 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

• 5+ 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
• 5+years of experience in model validation or model development with minimum Masters/Phd in a quantitative field such as applied math, statistics, engineering, physics, accounting, finance, economics, econometrics, computer sciences, or business/social and behavioral sciences with a quantitative emphasis.
• Able to demonstrate first-hand knowledge of advanced topics in various mathematical and numerical methods such as Monte Carlo, stochastic calculus, differential equations, linear algebra, applied probability, and statistics (Logistic regression, Time Series and Machine learning techniques);
• Familiarity in one or more of the following areas: Asset-liability risk management, Deposit models, including deposit balance, rate, and attrition forecasting, as well as depositor's behavior modeling, structured finance risk management and income forecasting (e.g., pooled collateral), and/or derivatives pricing or CVA/DVA modeling, Predictive modelling with Logistic regression, Time Series and Machine learning techniques.
• Strong mathematical, statistical, analytical, and computational skills along with programming skills
• Strong communication skills for a variety of audiences (other technical staff, senior management, and regulators) both verbally and in writing Capability to multi-task and finish work within strict timelines and provide timely requests for information and follow-up questions.
• Ability to work independently on complex model validations from start to finish.
• Attention to detail in both analytics and documentation

• Perpetual interest in learning something new but being comfortable with not knowing the all the answers.
• Aptitude for synthesizing data to 'form a story' and align information to contrast/compared to industry perspective