Lead Quantitative Analytics Specialist - Model Risk Management for Financial Crimes, Fair Lending and other Compliance models.

About this role:

Wells Fargo’s Model Risk Management (MRM) organization is seeking a highly qualified person to join its Decision Science and Artificial Intelligence (DSAI) Group as a Lead Quantitative Analytics Specialist (LQAS). The responsibilities of the DSAI Group include an end-to-end responsibility of managing the model risk over the model lifecycle including risk tiering, validation, and performance monitoring, etc.. In this specific position, the lead analyst shall focus on models built in house and by third parties primarily for the purpose of Financial Crimes, Fair Lending and other Compliance models.

MRM operates in a fast-paced work environment with continuously changing policies and technologies.  

Learn more about the career areas and lines of business at wellsfargojobs.com

In this role, you will:

• Lead end-to-end model risk management, including model validation, performance monitoring and governance for Financial Crimes, Fair Lending and other Compliance models.

• To be self- motivated, require minimal supervision, and produce work that is consistent with MRM’s recognized high standards.  

• To lead the validation projects and coach junior team members. 

• To be familiar with analytical data and sampling plans, various modeling frameworks, model replications, model performance assessments, test model development, model monitoring, and provide effective challenge to lines of business.  

• To develop reusable code and libraries to accelerate Financial Crimes model validation.

• To be practiced in writing detailed standard analytical reports to ensure Wells Fargo’s compliance with governance policies and regulations.

• To communicate with stakeholders, regulators, and auditors independently. 

• To be proficient with MRM framework and procedure. 

Required Qualifications:

• 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

Desired Qualifications:

• A PhD in statistics, economics, computer science, optimization, electrical engineering, or a related quantitative discipline

• In-depth knowledge of ML methodologies such as ensemble algorithms, neural networks, supervised and unsupervised learning.

• Strong in-depth knowledge of Financial Crimes, Fair Lending, and other Compliance models.

• Strong computing and programming background and knowledge of one or more languages such as Python and Java

• Experience with ML/AI computing platforms and tools 

• Ability to work with large datasets and some experience in database management and tools such as Hadoop, Spark and SQL

• Proficiency in software development

• Ability to take initiative and work independently in a structured environment.

• Excellent writing and communication skills for documentation and presentations to audiences of all technical backgrounds

• Strong conceptual and quantitative problem-solving skills and demonstrated ability to contribute independently; and

• Ability to work in cross-organizational projects and collaboratively with other groups.

Job Expectations:

• Willingness to work on-site at stated location on the job opening.

• This position offers a hybrid work schedule.

Pay Range
 

Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to achievements, skills, experience, or work location. The range listed is just one component of the compensation package offered to candidates.

Benefits

Wells Fargo provides eligible employees with a comprehensive set of benefits, many of which are listed below. Visit Benefits - Wells Fargo Jobs for an overview of the following benefit plans and programs offered to employees.

• Health benefits
• 401(k) Plan
• Paid time off
• Disability benefits
• Life insurance, critical illness insurance, and accident insurance
• Parental leave
• Critical caregiving leave
• Discounts and savings
• Commuter benefits
• Tuition reimbursement
• Scholarships for dependent children
• Adoption reimbursement

Posting End Date:

*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.

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.