Senior Quantitative Analytics Specialist

About this role:

Wells Fargo is seeking a Senior Quantitative Analytics Specialist for our DSAI(data science and Artificial intelligence) team.

In this role, you will:
• Perform highly complex activities related to creation, implementation, and documentation
• Use highly complex statistical theory to quantify, analyze and manage markets
• Forecast losses and compute capital requirements providing insights, regarding a wide array of business initiatives
• Utilize structured securities and provide expertise on theory and mathematics behind the data
• Manage market, credit, and operational risks to forecast losses and compute capital requirements
• Participate in the discussion related to analytical strategies, modeling and forecasting methods
• Identify structure to influence global assessments, inclusive of technical, audit and market perspectives
• Collaborate and consult with regulators, auditors and individuals that are technically oriented and have excellent communication skills

Required Qualifications:
• 4+ 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:
• 4+ years of experience with Master’s Degree or equivalent in computer science, electrical engineering, data science, or another discipline with a strong quantitative core and extensive exposure to software engineering or scientific computing
• Hands-on experience and deep knowledge in at least one of these areas: NLP and GenAI and/or Agentic models, with an exposure to model risk management policies and procedures preferably in financial domain.
• Change management skills and proven ability to conceptualize and communicate designs and plans;
• Knowledge of recent frameworks for Machine Learning, GenAI and Agentic pipelines.
• Hands-on experience with LangGraph, LangChain, Google ADK, Agentic Performance evaluation pipelines like Arize a plus.
• Deep understanding of data modeling and relational databases. Advanced SQL, including analytical functions and recent additions to the SQL standard;
• 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
• Intellectually curious, with an attention to detail.
• Good verbal and written communication skills as well as interpersonal skills;
• Ability to prioritize work, meet deadlines, achieve goals, and work in a dynamic and complex environment; and
• Ability to develop partnerships and collaborate with other business and functional areas.

Job expectations:
• Work at the intersection of technologies related to Data Science & AI/ML, Natural Language Processing and Generative AI including Agents and Skills and their application to Fraud model development and validation. The role requires strong statistical background and expertise in Quantitative Modeling. The responsibilities include, but are not limited to, the following:
• Managing model risk throughout the model lifecycle from model identification to model retirement through various tasks including risk ranking, model validation, change management and performance monitoring etc.
• Delivering high quality technical reports ensuring factual accuracy, conceptual alignment, critical assessment and compliance to all policies and procedures.
• Helping to develop and evaluate the architecture and foundational components of internal validation tools, developing fully operational prototypes, and deploying them in partnership with our Technology partners.
• Providing guidance to the broader MRM community on best model risk management practices by leading or contributing to efficiency, effectiveness and automation efforts.

Posting End Date:

28 Oct 2026

*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.