Feb 2024 – May 2025 · 1 yr 3 mos · Gurugram, Haryana, India · Hybrid

• Owned the end-to-end product roadmap for a cloud-based fraud monitoring platform, spearheading the migration from on-premise Hadoop infrastructure to Google Cloud Platform (GCP). Defined MVP scope, authored detailed user stories, and established scalable SOPs—reducing fraud signal latency and significantly improving real-time detection responsiveness.
• Led cross-functional Agile squads to design and deploy a hybrid fraud assessment engine, combining real-time machine learning scoring with batch-based heuristic evaluations. Strategically prioritized backlog initiatives to optimize detection accuracy, resulting in a reduction in false-claim approvals and a decrease in manual review volumes, enhancing both operational efficiency and risk containment.
• Partnered with stakeholders across credit and banking divisions to embed fraud controls into new product lines, ensuring seamless integration within evolving business architectures. Directed execution efforts in support of a Category 4 to Category 2 bank upgrade, striving to achieve regulatory compliance while strengthening enterprise risk posture.
• Additionally, consolidated cross-functional data warehousing requirements and drove enterprise-wide cloud enablement initiatives. Designed and delivered targeted training programs for 200+ professionals across data science, analytics, and engineering teams—accelerating BigQuery adoption and fostering a data-driven, cloud-first operating culture.

Jul 2022 – Jan 2024 · 1 yr 6 mos · Gurugram, Haryana, India · Hybrid

• Demonstrated deep expertise in translating complex business and risk requirements into scalable, high-impact proactive fraud prevention rules. Designed and continuously optimized rule frameworks with a focus on modular enhancements, rapid deployment cycles, and long-term maintainability — ensuring seamless integration into live operational workflows without compromising system stability.
• Defined and executed credit risk product strategy by leveraging behavioral modeling and rule-based feature engineering to identify early indicators of credit “bust-out” patterns. Integrated data-driven insights into actionable detection logic, strengthening proactive risk mitigation within the card ecosystem.
• Additionally, conceptualized and launched a retrospective fraud analytics capability, enabling structured performance evaluation and continuous improvement of detection strategies. Key contributions included:
• Authoring detailed Product Requirement Documents (PRDs) to align cross-functional stakeholders
• Coordinating User Acceptance Testing (UAT) with risk and operations teams to validate logic robustness
• Establishing a benchmarking framework for model backtesting, precision tuning, and performance measurement
• Enhancing analytical rigor through standardized evaluation metrics and feedback loops
• Through these initiatives, strengthened fraud detection precision, improved response agility, and contributed to a more resilient, data-driven risk management framework.

Jan 2022 – Jun 2022 · 5 mos · Gurugram, Haryana, India · Hybrid

• Played a foundational role in building the team from inception, contributing to its strategic direction, operating model, and execution framework. Spearheaded the creation and institutionalization of Standard Operating Procedures (SOPs), establishing structured governance, accountability, and scalable workflows to drive operational excellence.
• The team was instrumental in delivering risk intelligence solutions designed to proactively identify and mitigate emerging credit and fraud risks within the American Express card ecosystem. By embedding structured processes and analytical rigor, we strengthened early risk detection capabilities, enhanced decision velocity, and supported more resilient fraud prevention strategies.

Jun 2021 – Jul 2021 · 1 mo · Mumbai, Maharashtra, India · Remote

• Diagnosed and resolved long-standing inconsistencies in CRON jobs orchestrated through the Quartz .NET Scheduler, restoring stability and reliability across critical background processes.
• Conducted root cause analysis to identify scheduling conflicts, execution failures, and performance bottlenecks that were impacting downstream notification and alert workflows. Reconfigured and optimized job triggers, dependencies, and retry mechanisms to ensure consistent execution and fault tolerance.
• As a result, significantly improved the accuracy and timeliness of automated notifications and alert systems, enhancing operational efficiency, reducing system disruptions, and strengthening overall process reliability.