Prasad Dalvi
Senior Manager, DSA Modeling, TransUnion CIBIL
Builds the credit risk models a bank uses to decide whether to lend — the part of data science that has a consequence attached to it.
Prasad has spent eight years building machine learning and statistical models for banking and retail — credit risk scorecards, retail analytics, and the pipelines that feed them.
He is Senior Manager for DSA Modeling at TransUnion CIBIL, and has previously worked at Tiger Analytics, RBL Bank, Nielsen and Energos.
His day-to-day is PySpark, Python and SQL on Azure and AWS — but the harder half of the job is the part before the model: deciding what question is actually being asked, and whether the data can honestly answer it.
He holds an MSc in Applied Statistics and Analytics from NMIMS, a PG Diploma in Data Science from St. Xavier's, and a BSc in Statistics.
Most students hear 'data science' as a job title long before they see what the work involves. Prasad talks about the work.
What they talk about
- What a data science job actually involves, day to day
- How a credit risk model gets built — and how it gets checked
- The statistics you will genuinely use, and the statistics you will not
- Getting into analytics from a statistics or non-CS degree