- Enhanced yearly revenue by $15M for credit card product through building and deploying credit and fraud risk models- Evaluated and Integrated model risk governance KPI metrics using Tableau, 30% increase in reporting efficiency- Designed experiments and conducted A/B testing on credit risk strategies, improving credit card’s risk health by 15%- Developed the pioneering predictive model for card collection team; achieving a ~60% capture rate of charge-offs for high risk accounts, resulting in significant revenue preservation; deployed the model on DBT with a 6 member team- Automating personal loan loss forecasting pipeline using Python, projected to boost worktime efficiency by 60%- Mentoring 1 Data Scientist and 2 Analysts in constructing a comprehensive credit risk model, fostering team synergy

- Spearheaded the development and deployment of the inaugural portfolio credit risk model for credit cards projecting a yearly revenue increase of ~$5M; deployed the scoring pipeline along with a 4 member BI and strategy team- Designed Early Payment Delinquency (EPD) credit card model, ~10% expected improvement in delinquency rate- Partnered with a 8 member Engineering, Strategy and Credit Karma (CK) team to build End-to-End testing pipeline; deployed the model into underwriting and CK production environment for front and back end decisioning- Built a fraud model for our credit card using Threatmetrix, Emailage and Id Analytics vendor attributes; ~6% higher performance and ~5% improved capture rate compared to a third party industry benchmarked fraud score- Migrated 3 model monitoring pipelines from Big Query to DBT environment; ~30 % efficiency improvement- Conducted A/B testing and risk impact analysis of debt settlement enrolled accounts (DSA)

• Optimizing hard cuts and expected bad rates in the underwriting loan model to achieve target increase in approval rate• Building a predictive model to predict Same As Cash (SAC) rate and use the result for cashflow prediction • Analyzes the vertical and horizontal risk metrics for our loan and Lease-To-Own product to track companies’ health

• Performed vertical and horizontal risk analysis on credit models and policies• Improved production models for credit decisioning and fraud detection in our Lease-To-Own (LTO) business• Deployed Adverse Action business rules in production for the loan product; worked closely with the compliance team

• Developed profitability prediction model at ASIN-level granularity as part of the eCommerce Insights and Analytics team using proprietary business and Amazon vendor data; • Created a Tableau based decision support system for underperforming products• Performed k-means clustering for SKU segmentation and forecasted weekly sales using ARIMA and LSTM modeling; segmentation of SKUs and automation of 20% of the Analyst’s task improved computational efficiency• Implemented optimization modeling for maximizing profit by creating SKU bundles using Mixed Integer Linear Programming• Selected to present work as a poster at the 2020 INFORMS Business Analytics conference

Complaints analysis and customer segmentation• Extracted insights from customer complaints data using Minitab and Excel to provide inputs to New Product Development (NPD) and Operations teams. NPD used these insights to reduce 18% product-related complaints• Identified bottlenecks in service-related complaints by performing demographic Time Series analysis in Minitab. Led to an improve of 14% reduction in service-related complaints• Initiated customized daily complaint reports to capture customer voice of top Auto, OEM, Retail and Export customers Toyota, Honda, Ford, etc. Segmentation of customers resulted in a 7 % reduction in Auto complaintsImproving Customer Satisfaction Index (CSI)• Presented monthly complaints report of key concerns in major product basket to the executive committee meeting chaired by President, Vijayanagar Works. Overall CSI improved from 3.76 to 3.88 from FY15-16 to FY17-18• Demonstrated close coordination with 10+ cross-functional teams for complaints analysis; leading to 66% & 33% reduction in technical acceptance time, and rejection percentage of customer complaint respectively across 5 years• Collaborated with BAO Steel Team, China to integrate Integrated Process Quality Management System (IPQMS) and SAP-based customer complaints management system (CCMS)Contribution in the successful Deming Prize Journey• Responsible for driving various aspects of TQM including Daily Management, Policy Management, and Lean Manufacturing in customer complaints being amongst 20-member core team of Quality Assurance• Led a 5-member team during Lean Six Sigma Green Belt Project, and analyzed internal diversion data using statistical tools regression and hypothesis testing, resulting in a cost-saving of around $43,000 per annum

Characterization of mono-filamentary Bi-2212 superconducting wire, prepared by metallic precursor route.

Characterization and texture analysis of Nb3Sn based multi-filamentary superconducting wire using TEM, FESEM, XRD, EDS, and EBSD techniques.