Cars24 Financial Services(NBFC) - Data Scientist/Sr. Data Scientist

The Problem

Buying a used car is outright painful. After selecting a car you would want to purchase which rightly fits all your parameters, you need a loan to cover the financial gap between the full price of the car and the down payment you have in your hand right away. If it was a new car, any bank/NBFC would have funded the highest amount possible on invoice but when it comes to used car, they become restrictive and choosy. Less than 1/5th of the used car transactions are funded today.

Why is the problem worth solving?

The ratio of used cars v/s new cars bought every year is >10x in developed countries like USA and China as compared to mere 1.5x in India. Partly, that is because of the very few financing options available for the used car market. As Cars24 progresses to increase the used car transaction in India, our own NBFC will be the torch bearer in making the finance available for it to the masses.

We are selling more than 15k cars every month and it makes it super easy for us to re-liquidate the car in case of default. A customer can even return the car much earlier if he's not able to bear EMIs. This is the WOW factor we want to introduce to lending.

Third, auto business is not just transactions but is also, lending/ financing, servicing, insurance, data services, and so much more. So if we own supply, we can own the entire auto ecosystem.

So what can we create?

Nothing less than India's largest auto company. A company that helps car owners sell their car, buy another car, get loan, get insurance, get a car repaired, and so much more.

India's largest auto company that could be worth so many tens of billions in a market that is a $100bn addressable market today!

Job Details

- Get to work with exciting new age cutting edge problem statements related to used car financing in a fast growing NBFC arm of CARS24, CARS24 Financial Services.

- Get full business exposure by understanding the policy level problems and contribute to the strategy directly

- The customer segment and moats niche to CARS24 gives us a chance to understand the impact we create while understanding unique delinquency patterns

Major Responsibilities -

- Use data analyses and statistical techniques to develop solutions to improve customer experience and to guide business decision making

- Identify predictors and causes of business related problems and implement novel approaches related to forecasting and prediction

- Identify, develop, manage, and execute analyses to uncover areas of opportunity and present written business recommendations

- Collaborate with multiple teams as a leader of quantitative analysis and where you develop solutions that utilize the highest standards of analytical rigor and data integrity

- Analyze and solve business problems at their root

Problem statements we are working on

- True consumer only risk behaviour for Used-car and Used-bike

- Used-car asset risk underwriting

- Borrower-asset mismatch

- Income imputation from unconventional and alternate data sources

- Credit Limit Increase/Decrease (CLI/CLD)

- UCL-PD risk and IRR clearance based pricing

- LTV<>Tenure customization risk

- Repayment capability prediction to match loan requirements with minimum documentation

Must-Have Qualifications -

- B.Tech from a tier1/tier2 college

- At least 2 years of industry experience in data analysis, statistical analysis and predictive modeling

- Decent DS knowledge and analytical algorithms

- Prior Experience on SQL and Python

- Solid OOPS and project management skills

- Excellent Microsoft Office skills, including a strong working knowledge of Excel

- Problem solving ability and passion for big data

- Excellent communication and data presentation skills

Preferred Qualifications

- Experience with analysis tools like Tableau, Snowflake etc

- Exhibits business judgment

- Has relentlessly high standards

- Dives deep and is result oriented

- Thinks big and has convictions

- Works in a fast-paced environment where continuous innovation is desired and ambiguity is the norm

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