Quantitative Analytics Specialist

About this role:

Wells Fargo is seeking a Quantitative Analytics Specialist (QAS) position in Model Risk Management (MRM). MRM operates in a fast-paced work environment with continuously changing policies and technologies. The successful candidate as a QAS is expected to be self- motivated, require minimal supervision, and produce work that is consistent with MRM's recognized high standards. Effective 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 Retail Credit Loss models which cover Retail Auto & Unsecured and Small Business Lending. The models include wide variety of models such as Comprehensive Capital Analysis and Review (CCAR) stress testing, Current Expected Credit Loss (CECL and BAU. The candidate should able to work in validation of other models such as in Basel model, Wholesale Commercial models etc. 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.

• Performing model validations and clearly documenting evidence of validation activities
• Develop benchmarking and alternative models in Python/R/SAS
• Conducting model replication and review of model documentation
• Providing effective challenge to models developed in/for the lines of business, including identifying conceptual weaknesses in a model and understanding tradeoffs with other approaches
• Managing and mitigating model risk to meet or exceed regulatory and industry standards
• Assisting experienced validators and business partners for ad-hoc analysis and testing
• Communicating model risk findings and limitations to key stakeholders
• Contributing to the improvement of model building and model use practices
• Providing analytical support and offering insights regarding a wide array of business initiatives
• Interacting with senior management and regulators on key modeling issues, including the identification, management, and mitigation of model risk
• Proactively following the industry trend to apply and develop new technology as part of R&D to improve efficiency

• 1-5 years of experience in model validation/development or related data analytics are with minimum Masters 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. Experience in Banking domain is an added advantage.

• Strong mathematical, statistical, analytical and computational 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
• 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;
• Skill in managing relationships with key model stakeholders
• Eagerness to contribute collaboratively on projects and discussions
• Perpetual interest in learning something new, but being comfortable with not knowing the all the answers
• Attention to detail in both analytics and documentation
• Aptitude for synthesizing data to 'form a story' and align information to contrast/compare to industry perspective Intellectually curious, who enjoy solving problems.