Subhadeep Pal
About Subhadeep Pal
Subhadeep Pal is an Authorised Officer in Model Risk Management at UBS, where he has worked since 2019. He holds an MTech in Applied Statistics and Operations Research from the Indian Statistical Institute and has experience in data science and advanced analytics.
Work at UBS
Subhadeep Pal has been employed at UBS as an Authorised Officer in Model Risk Management since 2019. His role involves validating credit risk models, which are critical for investment banking portfolios. He specializes in various aspects of credit risk, including Probability of Default (PD), Loss Given Default (LGD), Credit Conversion Factor (CCF), Stress Loss, and Expected Tail Loss (ETL). His work contributes to the overall risk management framework at UBS, ensuring that models are robust and reliable.
Education and Expertise
Subhadeep Pal holds a Master of Technology (MTech) in Applied Statistics and Operations Research from the Indian Statistical Institute, Kolkata, where he studied from 2017 to 2019. Prior to this, he earned a Bachelor of Engineering (BE) in Electrical Engineering from Jadavpur University, Kolkata, completing his degree from 2013 to 2017. His educational background in both electrical engineering and applied statistics equips him with a unique interdisciplinary approach to model risk management.
Internship Experience
Before his current role at UBS, Subhadeep Pal gained practical experience through various internships. He worked as a Data Science Intern at LG Ads in 2019 for five months in Bengaluru, where he focused on data analysis. In 2018, he completed a two-month internship in Advanced Analytics at Cummins Inc. in Pune. Additionally, he served as a Trainee in Engineering at The West Bengal Power Development Corporation Limited (WBPDCL) for one month in 2016. These experiences provided him with foundational skills in data science and analytics.
Background
Subhadeep Pal has a diverse background that combines engineering and statistics. His education in electrical engineering and applied statistics allows him to approach model risk management from multiple perspectives. This interdisciplinary foundation supports his current role in validating complex credit risk models, enhancing the effectiveness of risk management practices in the financial sector.
Programming and Quantitative Skills
Subhadeep Pal possesses strong programming skills that enhance his capabilities in quantitative analysis and risk modeling. These skills are essential for validating and developing risk models, allowing him to effectively analyze data and implement statistical techniques. His expertise in programming complements his academic background and professional experience, making him proficient in addressing challenges in model risk management.