Srikant Ganguly

As a data analyst in Risk Appetite and Monitoring team of the Risk Management Group at Crédit Agricole - PFM, my missions consist of the following pillars: - Led regulatory projects and reporting (Cost of Risk Estimation & Forecast, Bad Loans, Collections, and Litigation) within the Risk Management Group, focusing on Credit Risk in a cross-functional technical role: • Developed dashboards (RWA, EAD, Cost of Risk, Defaulted Loans) for the Risk Appetite & Monitoring department and business lines, implementing KPIs and reporting solutions using Qlik Sense, SAS, Power BI, Python, MS Fabric, and Dataiku. • Collected and extracted data sources to support business teams, ensuring efficient data access and analysis with Python, Dataiku, and SAS. • Implemented automated processes and queries to enhance production monitoring and reporting, leveraging Python, Pandas, and ETL pipelines. - Calculated, monitored, and forecasted the Cost of Risk for 'Crédit Agricole - PFM' and its individual business units by leveraging historical data and robust mathematical models to enhance risk assessment and decision-making. - Ad-hoc Analysis: performed analysis and build dashboards to validate hypotheses and guide risk appetite roadmap with data. - Conducted ESG governance reporting for the ECB, comparing key risk indicators (CoR, Outstanding, etc.) for Green and Non-Green loans based on several factors, driving sustainability-focused decision-making within the financial risk framework. - Monthly database Production and Monitoring.

• Designed and delivered interactive DASHBOARDS with key financial indicators for Risk Appetite and Monitoring division under the Risk Management Group. • Ensured the quality, reliability, and maintenance of the Risk Datamart created within the department, optimizing data storage and accessibility for accurate risk analysis. • Collaborate with the corporate center to improve data use and enrich the data factory. • Implementation and automation of internal reporting.

•Simulated carbon nanotube behavior using ab initio methods (FORTRAN, Python), modeling non-linear dynamics in material systems, analogous to metastable states in physical distributions. •Applied optimization and numerical methods to validate outcomes, reducing simulation error and improving model interpretability. •Investigated data distributions under different stress models, analyzing energy states and convergence properties of solutions.