Senior Data Scientist - Credit Risk

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.