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 algorithmsboth 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 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 deploymentPrevious experience in PD / EAD / LGD model development / validationExperience in CSI / PSI model monitoring processHands 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 RegressionRandom forestXGBOOSTMarkov ChainPSI / CSI for model monitoringStrategy performance tracking and swap in / swap out analysisStrong entrepreneurial driveGood to have :
Good business understanding of the fintech / consumer finance spaceExperience in working with credit card / personal lending space, esp fintech hands on experience in working with Telecom data(ref : hirist.tech)