- 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 enhancements

Required 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

- Strong entrepreneurial drive

Good 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