Quantitative Analytics specialist

About this role:

Wells Fargo is seeking a Quantitative Analytics Specialist.

In this role, you will:

• Develop, implement, and calibrate various analytical models
• Perform highly complex activities related to financial products, business analysis and modeling
• Perform basic statistical and mathematical models using Python, R, SAS, C++ and SQL
• Perform analytical support and provide insights regarding a wide array of business initiatives
• Provide solutions to business needs and analyze workflow processes to make recommendations for process improvement in risk management
• Collaborate and consult with peers, colleagues, managers, and regulators to resolve issues and achieve goals

Required Qualifications:

• 2+ 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 statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline

Desired Qualifications:

• Proven experience as Model Validator/ Model Developer in the industry. Exposure to Banking Book is highly desirable

• Candidate should have a strong understanding of Balance Forecasting models /Loss forecasting models /PPNR/fee models /Econometric models.

• Candidate should have an excellent business understanding especially the Wholesale/Commercial portfolio (Banks, Corporate, Real estate, Specialized lending). The ideal candidate will bring in deep domain knowledge and advanced technical skills to drive sophisticated Credit risk validation initiatives across Commercial portfolio.

• Deep understanding on how different economic factors interact, how theoretical model translated into a mathematical equation, would  be  an added advantage.

• Candidate  should  be well versed with concept like Estimation, Backtesting, sensitivity, Shock/Scenario creation, Pandemic data management, coefficient stability  analysis.  

• Proven experience using techniques like Time Series Forecasting, Regression, Machine learning,

• Ideal candidate should be well versed in credit risk model validation/development of models (PD, LGD and EAD, LGD models, stress tests) for credit portfolio of the bank as per regulatory guidelines.

• Skill to apply regulatory requirements outlined in SR11-7, CCAR CECL/IFRS9, and other regulations and rules to conducting model validation assignments.

• Ability to develop comprehensive technical documentation including - Validation reports, Model risk  findings, Regulatory compliance documentation.

• Candidate must have hands on experience on Python, Pyspark and related libraries and code development skills (e.g., Python, Pyspark, Pyfarm) .

• Experience in performing model validations and clearly documenting evidence of validation activities. Communicating model risk findings and limitations to key stakeholders (preferred).

• 4+ years of experience with minimum Masters in a quantitative field such as Statistics/ Econometrics/ Finance/Computer sciences or any other behavioral sciences with a quantitative emphasis.

Job Expectations:

• MRM serves as the second line of defense to ensure the integrity of Wells Fargo’s model inventory.

• A Senior Quantitative Analytics Specialist is an individual contributor role.

• This position will focus primarily on Commercial Credit and Corporate Economic group model validations.  A validation project typically begins and ends with the analyst, requiring broad and continuous attention to detail, comprehensive documentation, and interactions with developers. 

• A Senior Quantitative Analytics Specialist should have a deep academic knowledge, broad based approach to solutioning business problems. He/she should approach the problem agnostic of analytic technique, tool or process. Ability to think outside the box and provide ensemble solutions should set them apart to be a high performing team member.

Posting End Date: 

23 Nov 2025

*Job posting may come down early due to volume of applicants.

We Value Equal Opportunity

Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.

Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.

Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.

Applicants with Disabilities

To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.

Drug and Alcohol Policy

 

Wells Fargo maintains a drug free workplace.  Please see our Drug and Alcohol Policy to learn more.

Wells Fargo Recruitment and Hiring Requirements:

a. Third-Party recordings are prohibited unless authorized by Wells Fargo.

b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.