Airtel - Principal - Credit Risk Analyst

Airtel Digital is hiring for a Data Science professional who is passionate to work in the area of financial services (credit risk) business.

Ever dreamed of working with 2-5 trillion records / day on a 200+ petabyte base?

Want to improve experience of ~400 million consumers using machine learning?

Do you want to work with one of world's largest telco and India's top brand?

If so, we would like you to join us on an exciting journey to create significant impact using data. The mission of the Data Science team is to transform terabytes of data into robust models for personalization and discovery. See why we call it India's Best Data Challenge: http://bit.ly/IndiasBestDataJob

Your work will directly impact the way millions of Indians use our Digital offerings. We would like you to join us on an exciting journey to create advanced ML products.

Responsibilities:

1. Work on complex business problems related to underwriting and risk.

2. Designing and curating the digital lending journey process of Airtel Thanks app which focusses on providing instant personal loans/Credit Cards to consumers across various areas like User Onboarding, KYC, Risk & Fraud checks, Loan Terms, Banking & Repayment setup etc.

3. Formulating Credit Risk policies, Fraud checks and control, pre-qualification parameters, user lender match and routing mechanism thereby ensuring a control on the quality flow

4. End to end development and testing of the solutions as per the requirements.

5. Provide analytical solutions through Segmentation modeling, credit policy and strategy, reporting and data analysis for the Airtel businesses

6. Monitor, maintain and improve all scorecards, policies and processes across portfolios and ensure its effectiveness

7. Deep dive into monthly business outputs for analyzing TTD mix, business conversions, early risk indicators with respect to various customer segments

8. Monitoring the collection strategies for different loan buckets, cost optimization strategies, review of delinquent buckets for certain irregularity trends to accordingly adjust the credit risk policies for better portfolio health

9. Creating User Segmentation and profiling users based on several attributes for better understanding of different profiles and accordingly provide differential treatment and offerings to each bucket

10. Strengthening Risk Infrastructure system by conceptualization, designing variables and schema for setting up of bureau mart, application mart, loan performance data mart, etc.

11. Support any adhoc deep dive data analysis on portfolio matrices

12. Track and improve key performance indicators, losses and portfolio quality. Provide deep dive analysis on portfolio matrices.

13. Build profile segmentation models like Application Score, Behaviour Score, etc..

14. Work closely with business team to understand their need and provide Analytical solution.

15. Assess if any early warning signals using data analysis and segmentations and take pro-active policy actions as and when required

16. Support in managing and improving various offer strategies, control offer generation and distribution through data analysis

17. Work closely with Product, Growth and IT teams to support business growth and drive new initiatives

18. Ongoing liaising with IT, Credit and BIU/DATA teams to ensure all policies, processes, data flow are working efficiently and all required changes are build and implemented suitably

Qualifications :

As a successful applicant, you should have:

1. BBM or MBA in Finance/ Statistics / Applied Finance .

2. Deep understanding of underwriting /risk policy .

3. 6 to 10 years of experience with strategy in credit risk and underwriting. Experience in designing and deploying low quality leads to better approval rates

4. Must be fluent with SQL .

5. Ability to contribute to multiple projects simultaneously and work in a fast-paced, collaborative and iterative environment.

6. Minimum 6 years relevant analytical experience in Scorecard understanding, Segmentation and Clustering.

7. Preferred Coding languages: SQL, R, Python ,

8. Experience in handling huge data base and the ability to do root cause analysis.