Senior Manager, Fraud Analytics & Data Science

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Senior Manager, Fraud Analytics & Data Science - Onsite in New York

Ready to build the future with AI?

At Genpact, we don’t just keep up with technology—we set the pace. AI and digital innovation are redefining industries, and we’re leading the charge. Genpact’s AI Gigafactory, our industry-first accelerator, is an example of how we’re scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform m large-scale models to agentic AI, our breakthrough solutions tackle companies’ most complex challenges.

If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what’s possible, this is your moment.

Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions – we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation, our teams implement data, technology, and AI to create tomorrow, today. Get to know us at and on Linked In, X, You Tube, and Facebook.

Job Description :

Inviting applications for the role of Senior Manager, Fraud Analytics & Data Science

Genpact is looking for a seasoned professional to join as a Senior Manager – Fraud Analytics & Data Science. This role is critical in driving data-driven fraud prevention strategies, developing advanced machine learning models, and delivering actionable insights to safeguard the bank’s products, platforms, and customers from evolving fraud threats.

Responsibilities :

This role sits at the intersection of fraud risk management, data science, and business strategy, working across various lines of business including Retail Banking, Payments, Cards, and Digital Channels.

• Build and enhance fraud detection systems using statistical and machine learning techniques.
• Monitor real-time and batch data to proactively detect fraud trends, anomalies, and suspicious patterns.
• Design and maintain fraud rules and scoring logic for transaction monitoring systems.
• Advanced Analytics & Modeling:
• Develop supervised and unsupervised machine learning models for fraud prediction (e.g., logistic regression, decision trees, random forests, XGBoost, neural networks).
• Perform data wrangling, feature engineering, model training, validation, and performance monitoring.
• Use anomaly detection, clustering, and network analysis to detect sophisticated fraud behaviors.
• Work with data engineers and IT to ensure access to high-quality, real-time data feeds.
• Design and build fraud-related data marts, dashboards, and alerting mechanisms.
• Collaborate with fraud operations, compliance, IT, business units, and external partners.
• Ensure alignment with regulatory expectations, model risk governance, and internal audit requirements.
• Present insights and fraud risk updates to senior management and fraud committees.
• Explore and implement new technologies (e.g., graph analytics, behavioral biometrics, NLP) for advanced fraud detection.
• Stay current with fraud typologies, threat intelligence, and global fraud trends.

Qualifications we seek in you!

Minimum Qualifications

• Bachelor’s or Master’s in Data Science, Computer Science, Mathematics, Statistics, Engineering, or a related field.
• Relevant years of experience in fraud analytics, data science, or financial crime risk management within banking or fintech.
• Hands-on experience building and deploying ML models in a production fraud detection environment.
• Strong command of Python, R, SQL, and data science libraries (pandas, scikit-learn, Tensor Flow, etc.).

Preferred Qualifications/ Skills

• Exposure to real-time fraud systems (e.g., SAS Fraud Management, Actimize, Falcon, etc.) is highly preferred.
• Experience working with large datasets, Hadoop/Spark, and cloud platforms (AWS, GCP, Azure) is a plus.
• Familiarity with tools like Tableau, Power BI, or similar for visualization and reporting.

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