Lead

About the Role

We are seeking a highly motivated and hands-on Lead AI & Data Science to establish and drive the Bank's AI & Data Science capability.

The role will initially focus on ownership and enhancement of the RBIH MuleHunter platform, fraud analytics, machine learning models, and Business Rules Engine (BRE), while also supporting the Bank's Enterprise AI initiatives and future AI adoption roadmap.

This is a unique opportunity to build and scale AI capabilities within a leading BFSI organization and work on high-impact use cases across Fraud Risk Management, Risk Analytics, Customer Analytics, Operations, and Compliance.

Key Responsibilities

Fraud Analytics & MuleHunter

• Own and manage the MuleHunter platform and related fraud analytics capabilities.
• Monitor and enhance model performance.
• Perform model tuning, retraining, validation, and optimization.
• Analyze false positives and false negatives.
• Develop and enhance fraud detection scenarios.
• Support fraud investigations through advanced analytics.
• Establish internal ownership and reduce dependency on external parties.

Business Rules Engine (BRE)

• Maintain and enhance fraud detection rules.
• Design and implement new fraud monitoring scenarios.
• Evaluate rule effectiveness and recommend improvements.
• Support testing and deployment of rule changes.
• Ensure appropriate governance and documentation.

AI & Machine Learning

• Design, develop, deploy, and monitor machine learning models.
• Build predictive and advanced analytics solutions.
• Identify and implement AI/ML use cases across business functions.
• Partner with business teams to solve business problems using analytics and AI.
• Establish AI/ML best practices and reusable frameworks.

Enterprise AI Initiatives

• Support Enterprise AI Platform implementation and adoption.
• Participate in AI/ML technology evaluations and proof-of-concepts.
• Contribute to AI architecture and solution discussions.
• Support identification and prioritization of AI use cases.
• Assist in future Generative AI and emerging AI initiatives.

AI Governance & Model Management

• Maintain model inventory and documentation.
• Support model governance and validation processes.
• Ensure compliance with internal policies and regulatory expectations.
• Support audit, risk, and compliance reviews.

Stakeholder & Vendor Management

• Work closely with business, technology, risk, compliance, and fraud teams.
• Coordinate with vendors, consultants, and implementation partners.
• Ensure effective knowledge transfer and capability development.
• Drive cross-functional collaboration for successful project delivery.

Mandatory Experience

• 8-12 years of overall experience.
• Minimum 5 years of hands-on experience in Machine Learning, Data Science, or Advanced Analytics.
• Experience in Banking, Financial Services, FinTech, Payments, Fraud Analytics, AML, Risk Analytics, or related domains.
• Experience building and supporting production-grade machine learning solutions.
• Experience working with business stakeholders and cross-functional teams.

Must-Have Skills

Domain

• BFSI / Banking / Financial Services / FinTech / Payments.
• Fraud Analytics OR AML OR Transaction Monitoring experience.
• Exposure to production support and operational ownership of analytical solutions.

Technical

• Python
• SQL
• Data Analysis
• Statistical Modelling
• Machine Learning
• Predictive Analytics
• Classification Models
• Anomaly Detection
• Feature Engineering
• Model Validation
• Model Tuning

Model Lifecycle

• Model Development
• Model Deployment
• Model Monitoring
• Model Performance Optimization
• Model Documentation

Functional

• Stakeholder Management
• Problem Solving
• Business Requirement Understanding
• Vendor Coordination
• Communication & Presentation Skills

Preferred Skills

AI & Advanced Analytics

• Fraud Analytics
• AML Analytics
• Financial Crime Analytics
• Risk Analytics
• Customer Analytics

Enterprise AI

Exposure to:

• Enterprise AI initiatives
• AI platform implementation or evaluation
• AI governance
• AI architecture discussions
• AI use-case prioritization

MLOps

Exposure to:

• Automated model deployment
• Model monitoring
• CI/CD concepts
• Workflow orchestration
• Model lifecycle management

Generative AI

Exposure to one or more of the following:

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• AI Assistants / Copilots
• Agentic AI concepts

Educational Qualification

• B.E. / B.Tech.
• MCA
• M.Tech.
• Master's Degree in Statistics, Mathematics, Computer Science, Artificial Intelligence, Data Science, Analytics, or related disciplines.

Relevant certifications in AI, Machine Learning, Data Science, Analytics, Fraud Risk Management, AML, or Financial Crime Compliance will be an added advantage.

Ideal Candidate

We are looking for a hands-on AI & Data Science professional who can:

• Independently own and enhance fraud analytics platforms.
• Build, deploy, and optimize machine learning models.
• Drive AI adoption across business functions.
• Engage effectively with business and technology stakeholders.
• Support Enterprise AI initiatives and future AI adoption.
• Build scalable AI capabilities within a highly regulated BFSI environment.