Key Accountabilities:

- End-to-end risk model lifecycle management: develop, deploy, and monitor.

- Data extraction using SQL/PySpark SQL; data cleaning and feature engineering.

- Model building using PySpark/Python on Spark.

- Proficiency in Logistic Regression, Random Forest, XGBoost, Markov Chain.

- Strategy performance tracking and swap-in/swap-out analysis.

- Develop innovative credit risk, churn, and usage models using mobile wallet transaction data, call data, telecom usage data, and customer bureau data.

- Partner with the Data Engineering team to define data pipelines to build and enhance the feature bank for ML algorithms in batch and real-time use cases.

- Collaborate with credit policy/portfolio management teams to drive P&L outcomes.

- Build reports for model monitoring and drive enhancements.

Mandatory Skills:

- Expertise in Big Data/ML: Build cutting-edge credit, churn, and usage models using advanced Machine Learning algorithms in a Big Data environment.

- Hands-on experience and track record of delivering projects in an individual capacity.

- Process-oriented: Help build processes that maximize operating efficiency while maintaining risk across multiple lending cycles.

- Obsession with collecting and analyzing data to drive business iterations and improvements.

- Willingness to go above and beyond: Ability to take on calculated risks and deal with ambiguity in a start-up environment.

- Hands-on experience in credit, fraud, and churn model development & deployment.

- PD/EAD/LGD model development and validation.

- CSI/PSI model monitoring process experience.

- Track record of delivering projects independently.

- Hands-on experience in data extraction using SQL/PySpark SQL; data cleaning, feature creation, and building models using PySpark/Python on Spark.

- Strong entrepreneurial drive; ability to handle ambiguity.

Desirable Skills:

- Business understanding of the fintech/consumer finance space.

- Experience in credit card or personal lending, especially in fintech.

- Hands-on experience working with Telecom data.