Job Responsibilities :- Own the end-to-end delivery of models/algorithms across customer life cycle; Develop innovative credit risk / churn / usage models using mobile wallet transaction data, Call data, Telecom usage data, customer bureau data etc- Partner with the Data Engineering team to define the required data pipelines to build and enhance the feature bank (foundational capability) to build/deploy the various ML algorithms both for batch and real time use cases- Collaborate with credit policy/portfolio mgmt. team to drive P&L; outcomes.- Building reports for model monitoring and drive enhancementsRequired Qualifications & Skills :- Solid expertise in end-to-end risk model lifecycle management (develop, deploy, monitor)- Previous hands on in credit, fraud,
churn model development and deployment- Previous experience in PD/EAD/LGD model development/validation- Experience in CSI/PSI model monitoring process- Hands on experience in data extraction using SQL/Pyspark SQL; data cleaning, feature creation and building models using PySpark/Python on Spark; Scale will be a plus. Previous exposure to below algorithms (preferably multiple) : - Logistic Regression- Random forest- XGBOOST- Markov Chain- PSI/CSI for model monitoring- Strategy performance tracking and swap in / swap out analysis- Robust entrepreneurial driveGood to have : - Good business understanding of the fintech/consumer finance space- Experience in working with credit card / personal lending space, esp fintech hands on experience in working with Telecom data (ref:hirist.tech)