Nov 2022 – May 2025 · 2 yrs 6 mos · Toronto, Ontario, Canada

• Lead a project to integrate International Credit Reports; collaborating with the technical team to establish KPIs for new credit card acquisitions.
• Developed Credit Card Limit Increase Strategy for New to Canada customers using Decision Tree Modeling, targeting 40K applications and CAD 48Mn annual exposure, achieving a 5% uplift in booking rates.
• Created grid-based strategy in Decision Modeler for low-income foreign workers, projecting a CAD 3Mn annual exposure increase.
• Designed Power BI dashboards for unsecured product originations, delinquency, and performance metrics, providing actionable insights for management.
• Delivered Retail Pre-Approved Campaigns for Unsecured Lending Products, generating 4.5Mn leads and achieving a 10% month-over-month business uplift.
• Implemented Decision Tree Model for deposit-only customers, increasing Small Business Pre-Approved Campaign leads by 2% monthly.
• Built and deployed XGBoost in Python as a Challenger Strategy for optimizing credit card portfolio line and approval decisions.

Jul 2021 – Oct 2022 · 1 yr 3 mos · Bengaluru, Karnataka

• Analytics Centre of Excellence | Client: - Leading Bank of Abu Dhabi
• Delivered credit risk models, including IFRS9 PiT PD regulatory models for Retail portfolios
• (Mortgage and Personal Loans). Implemented a macroeconomic series-based model in R and Python,
• reducing Expected Credit Loss (ECL) by 15%.
• Developed strategic segmentation for the corporate portfolio, introducing the ARIMAX model as a challenger for predicting portfolio default rates.
• Automated Bureau Character Generation, implementing around 1k features for Scorecard development and bureau-related analysis in SAS EG.
• Maintained effective client liaison for the successful delivery of critical deliverables.
• Implemented an Application Scorecard for Mortgage Loans and assisted a team of 2 in exploratory data
• analysis and data preparation activities.

Apr 2020 – Jun 2021 · 1 yr 2 mos · Dubai, United Arab Emirates

• Credit Risk Models | Client: - Leading Bank in Dubai
• Validated and developed efficient Application and Behavioral Scorecards for various asset portfolios
• (Credit Cards, Personal Loans, Auto Loans) following Central Bank guidelines in SAS EG and E-miner.
• Identified and rectified loopholes, optimizing the solution for an improved acceptance rate and a significant 4% increase in booking rate.
• Conducted on-site visits for business meetings and executed assignments in collaboration with the client.
• Identified significant events and attributes in time series data using market basket analysis in SAS Viya.
• Developed and presented a Deep Learning Model for determining propensity in Credit Card cross-sell campaigns based on timestamp data.

Apr 2019 – Apr 2020 · 1 yr · Mumbai, Maharashtra, India

• Formulated strategic solutions for acquisitions, deepening, and attrition campaigns, enhancing campaign origination results and incremental portfolio growth.
• Developed predictive models for customer funding, product cross-sell, and service activation, contributing to a notable 10% increase in regular campaign performance.
• Analyzed campaign performance and marketing growth data, presenting valuable insights to management.
• Engineered predictive models for diverse merchant categories, achieving an average incremental response between 5% to 8% for each category.

Jun 2017 – Mar 2019 · 1 yr 9 mos · Mumbai, Maharashtra, India

• Improved recommendations, resulting in an increased average ticket size for spending.
• Identified optimal communication channels (SMS/E-mail/Push) for customers, improving campaign
• effectiveness by 20% and enhancing ROI conversion.
• Developed propensity models for cross-selling various TPP products (Life Insurance/General Insurance,
• Mutual Fund) and optimized old scorecards.
• The overall solution effectiveness led to a substantial 15% quarter-over-quarter improvement in acquisition.
• Segmented and profiled customers for various classification problems, identifying opportunities using
• techniques like Text Mining and machine learning algorithms (CART, CHAID, Random Forest, etc.).