Sulabh Jain

Model Development responsibilities include. Managed and led a team of data scientists and analysts, developing end-to-end ML models for IFRS9 models Responsibilities include Probability of default models – Default identification (Churn Models) Exposure at Default (EAD) – Value estimation using Linear Regression models Application (Acquisition) scorecards – Cross Sell / Up Sell Behaviour scorecards (ECM) – Retention Models Further developing credit card behaviour models to identify and segregate transactors and revolvers utilizing advanced ML models Classification and identification of early SME loans based on behavioural aspects Detailed validation of model performance and model performance/market scenario/MEV related management overlays / PMAs Actuals, Forecasting and Stress Testing Impairment provisioning (Model Owner) responsibilities include Leading model redevelopment and existing model enhancements Ensuring regular model monitoring and model validations PMA / Overlays application to address model / macroeconomic volatility based deficiencies in the model results Identifying material Impairment drivers – model result analysis, customer features, MEV scenarios, portfolio deterioration, sectoral stress, new/matured exposures, etc. and presenting the analysis to senior Portfolio MDs and obtaining Sign-Offs on regular P&L Provisioning at various forums Leading a 10 member team managing IFRS9 complaint for unsecured (Credit Cards, Personal Loans) and secured (SME) for Portfolio Analytics & Insights team Managing business relationship with Risk Oversight, Portfolio Partners, Model Developers, Model Monitoring, Validation, Finance Controllers, Risk Reporting, Group Reporting, Audit, Regulators and IT

Model development for customer / retail banking Statistical model development to predict and identify full payer and revolver behaviour of the credit card holder Classification model to identify the early attrition (closure) on loans (personal/home loan) Retail customer profiling and segmentation based on month on books and first engagement with the bank Identify cross sell opportunities based on product holding pattern Customer identification, Customer Acquisition, Customer Engagement and Customer Retention models Loyalty data analysis – Merchant level, Product level, points earned, points redeemed; to identify segments and device effective campaigns for further effective customer communication Statistical model development of Credit Risk PD model, Application (Acquisition) scorecards and Behaviour scorecards (ECM) – Retention Models for Credit Cards portfolio. Leading a 7 member solution delivery team covering analytics advisory, data science and business intelligence solutions.

Hands on statistical model development and model validation of Credit Risk Capital Models (Business to Consumer analytics) catering to Probability of default models – Default identification (Churn Models) Exposure at Default (EAD) – Value estimation using Linear Regression models Application (Acquisition) scorecards – Cross Sell / Up Sell Behaviour scorecards (ECM) – Retention Models Leading a 7 member solution delivery team covering analytics advisory, data science and business intelligence solutions. Stakeholders involve from European geographical locations like Poland, Spain and Germany. Ad-hoc statistical analysis to support insights / recommendations of the validation report to the business and strategy teams.

Working on advanced statistical models involving customer retention models (Behaviour Scorecards), customer churn models (default identification), Cross Sell Models, Value Estimation models for a Multinational UK Bank while leading a team of 10 members. Role also involved validating the performance of the currently deployed and / or in use statistical models by various stakeholders for various business requirements based on statistical parameters like Discriminatory Power (Gini), IV (Information Value), PSI / CSI, Accuracy analysis, Rank Ordering, Scorecard Cut-off analysis and others. Conducting ad-hoc statistical analysis to support the insights and recommendations presented to business and strategy teams. Achievements: Team award for the most proficient and promising team of the Quarter; Manager of the Quarter Award

Developed linear regression models for finding out the impact / effectiveness of various promotional and marketing / media vehicles for a CPG major (Market Mix Models) Developed Logistics Regression and Random Forest models for a retail giant, to measure the readiness of the loyalty card customers to acquire the card (Cross Sell Model) Drivers analysis modeling using Graphical Probabilistic Networks and Machine learning based on Bayesian Belief methodology for a technology major Worked on customer segmentation, profiling and targeting to analyse customer behaviour based on constructs laid according to business and data understanding for the retail client Catering to CPG/Retail, Technology and E-commerce industry Setup a visual dashboard using Spotfire (to monitor real time performance of a product launch) for a CPG client involving customer funnel analysis Segment deep dive analysis of a major CPG client for its Japan industry and also analyzing business behavior in terms of performance based on various business parameters Worked on pre-sales proposals and had 50% conversion rate, catering to CPG/Retail clients in US market Work responsibilities were extended to project scoping, story building, project timeline discussions, preparing project plan and helping / training junior members of the team Achievements: Multiple excellence awards in showing exceptional understanding of business requirements, timelines and client requirements; Appreciation mail from CEO (Srikanth Velamakanni) on showing exceptional performance and showcasing exemplary commitment, work ethic and great work

Worked as a Senior Analyst supporting the internal projects. Analyzing the scope of improvement for future deliverables in comparison to the existing implemented process / methodology

Market research & analyzing new business channels Prospecting and analyzing new channel partners in respect to company domain

Analysing the business problem, conducting exploratory and deep dive analysis Preparing the data using SAS, to be used for further modeling prospective catering to business needs Deep dive analysis and profiling of various business lines, involving correlation analysis, data quality and data harmonization checks (Missing value treatment, Imputations, etc.) Achievements: Certificate of excellence from client and Bronze award in showing exceptional efforts in data analysis