Mar 2026 – Present · 2 mos · Bengaluru, Karnataka, India

• Working on large scale compliance and risk analytics, supporting Client Risk Rating (CRR), sanctions screening, and transaction monitoring use cases across multiple geographies.
• Hands on with an internal Machine Learning Platform (MLP) leveraging Jupyter based development workflows for experimentation, analysis, and model iteration under enterprise security constraints.
• Contributing to the evolution of supervised machine learning models for client risk classification, trained on historical SME labeled data and engineered from quantitative and categorical risk signals sourced from enterprise BI systems.
• Performing feature analysis, data quality assessment, and model evaluation to support next generation CRR model enhancements (CRR 2.0).
• Supporting tree based gradient boosting models (e.g., XGBoost/LightGBM/CatBoost) with a focus on calibration, segmentation, and regional performance analysis.
• Partnering with SMEs and model owners to translate regulatory and domain knowledge into robust, explainable ML features and modeling strategies.
• Contributing to model versioning initiatives, including region specific model strategies (e.g., US vs Non US) to improve predictive accuracy and risk differentiation.
• Operating within strong model governance, audit, and documentation frameworks, ensuring reproducibility, traceability, and compliance with internal model risk standards.
• Actively building domain expertise in AML typologies, sanctions screening challenges, and investigation workflows, with a learning first, responsibility driven approach.

Jan 2022 – Present · 4 yrs 4 mos · Bengaluru, Karnataka, India

• Created multiple AI Agents aligned with job role and reduce my work efforts by 25% using AI agents
• Collaborate with Visa Research and innovation teams on Generative AI, NLP, and data-driven technology initiatives.
• Analyze large volumes of technical and market data to identify patterns, innovation trends, and AI adoption opportunities.
• Apply machine learning and statistical thinking to evaluate feasibility, scalability, and impact of AI-driven solutions.
• Translate unstructured technical ideas into structured frameworks, enabling defensible AI and data-centric solutions.
• Work closely with cross-functional stakeholders to support research-to-product decision making.