Lead Quantitative Analytics Specialist - Python/SQL/Machine Learning

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.