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

Overview

Lead the end-to-end development, validation and monitoring of complex fraud risk models using advanced statistical and machine learning techniques, supporting the Chief Operating Office's focus on risk and controls.

What you'll do

• 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

What you'll need

• 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

Nice to have

• 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
• 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

Details

• Location: Bengaluru, India
• Work mode: Hybrid, in accordance with business requirements
• Working hours: 1:30 PM to 10:30 PM IST shift, with flexibility based on business needs
• 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