Lead Quantitative Analytics Specialist - Python, SQL, Machine Learning Model Building, Gen AI

About this role:
Wells Fargo is seeking a Lead Quantitative Analytics Specialist who is passionate about using data and advanced analytics to solve complex business challenges. In this role, you will have the opportunity to deliver actionable insights that support informed decision-making and contribute to the Chief Operating Office’s focus on strong risk and controls, execution discipline, operational excellence, and customer centricity.
In this role, you will:
Lead the end-to-end development, implementation, validation, documentation, and ongoing monitoring of complex fraud risk models using advanced statistical and machine learning techniques
Design and deploy fraud detection models across card transactions, applications, checks, payments, and other fraud risk domains
Analyse large, complex structured and unstructured data sets, perform advanced feature engineering, and identify predictive variables that improve fraud detection and risk outcomes
Evaluate complex and emerging fraud patterns using graph and network analytics, anomaly detection, ensemble modelling, and other advanced analytical methodologies
Assess new data sources, modelling techniques, and technologies, and recommend solutions that strengthen fraud detection capabilities and support broader business objectives
Exercise independent judgment in addressing complex, multifaceted analytical challenges, evaluating model performance, and balancing fraud risk, customer impact, and operational considerations
Ensure models comply with model governance, documentation, validation, regulatory, and risk management requirements throughout the model lifecycle
Prepare comprehensive Model Development Documents and validation responses, and articulate and defend modelling assumptions, methodologies, limitations, and results during independent reviews
Partner with Model Risk Management, Fraud Strategy, Operations, Product, and other stakeholders to resolve complex issues, align priorities, and translate analytical findings into actionable business recommendations
Lead multiple concurrent fraud analytics initiatives, establish model development and coding best practices, and provide technical direction and mentorship to less experienced data scientists and fraud analysts.
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
Bachelor's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science
Experience building, developing, or implementing machine learning models
Hands-on experience with Python and SQL for data analysis, quantitative modelling, or related applications
Experience with Generative AI technologies and their application to analytical or business use cases
Desired Qualifications:
Experience applying advanced analytics or machine learning within fraud, financial crimes, risk, or related areas
Experience developing analytical solutions using large-scale data and cloud environments
Familiarity with model deployment, MLOps, or model lifecycle practices
Preferred Master’s degree in a quantitative or related field
Experience in banking, financial services, or product-based technology
Ability to turn complex analytical findings into clear, actionable insights
Experience partnering across business, technology, risk, or model governance teams on complex initiatives
Job Expectations:
Ability to work a 1:30 PM to 10:30 PM IST shift, with flexibility based on business needs
Ability to work in a hybrid work environment in accordance with business requirements
Partner closely with Model Risk Management (MRM) teams throughout the model lifecycle
Collaborate with Fraud Strategy, Operations, and Product teams to support analytical and business priorities
Effectively communicate analytical insights and recommendations to technical and business stakeholders.
Posting End Date:
19 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.