Project Summary: Developed a data-driven product recommendation system by generating over a million synthetic records, building a Neo4j graph database and creating predictive models to analyze product, customer and risk metrics.• Created synthetic data of over a million records with 50+ attributes and developed a graph database in Neo4j to analyze product, customer, risk, and marketing aspects effectively.• Conducted feature engineering and sampling to derive meaningful features from raw data of around 1 million, enhancing model performance through rigorous data preprocessing and feature selection.• Performed exploratory data analysis (EDA) and implemented queries and algorithms, including K-Nearest Neighbors (KNN) with cosine similarity, to maximize data relevance and usability.• Collaborated with stakeholders to develop predictive models for recommendation systems based on graph data in Python, leveraging libraries such as NumPy, Pandas and the Graph Data Science (GDS) package.

• Steered an 8-member analytics team, supporting 2,000 staff and 4 million customers across 430 branches, improving data-driven decision-making by 30 % and customer service efficiency by 25%.• Engineered scalable data models and ETL pipelines using Python and SQL, implementing Random Forest and Gradient Boosting algorithms to optimize credit assessment, driving $15 million in revenue growth.• Implemented comprehensive A/B testing program across products and platforms, analyzing 500,000 + user interactions, resulting in 18% increased digital engagement, 7% reduced churn, and $2 million annual revenue impact.• Developed over 40+ business dashboards for various campaigns, business and KPIs using SQL, HTML, CSS and Tableau.• Devised data-driven customer segmentation models and targeted strategies to identify the top 5–25 non-performing assets (NPA) customers per branch, streamlined the disposition of compromise proposals, and reduced the gross NPA by USD 10 million.• Spearheaded data analytics and business intelligence initiatives, developed comprehensive data processing workflows, improved Know Your Customer (KYC) compliance from 57% to 83%, and customer digitization from 33% to 54%.• Managed relationships with 15+ external stakeholders, including regulatory bodies, network providers, and hardware partners, ensuring over 90% SLA compliance, reducing penalties by 20%, saving $100,000 annually.• Delivered diverse projects in business strategy and implementation, financial fraud detection and prevention, building forecast models, cross-selling products, statutory audits, and communication with senior management.• Designed and implemented a comprehensive onboarding program for 100+ new hires, encompassing business strategy, technology systems, and operational solutions.

• Conducted competitive analysis and market segmentation for 15+ business intelligence projects, enhancing actionable insights by 20% and improving client decision-making accuracy by 25%.• Leveraged SQL and Tableau to analyze large datasets in Consumer Goods, Food, and Healthcare industries, uncovering market patterns and improving prediction accuracy by 15%.

• Motorola Mobility : Implemented and customized Oracle applications, leading to a $50,000 annual cost savings for client.• Xerox Corporation Constructed RICE (reports, interfaces, conversions, enhancements/extensions) components using SQL, PL/SQL, Oracle Reports, Forms, and Workflows and cut down person-hours by 30%.• Provided L3-level support to troubleshoot and fix software bugs, and net result is a 15% drop in customer complaints.• Created and maintained comprehensive test cases, ensuring a high level of quality control and slashing down defects by 20%.