Head - Credit Risk - Fintech Startup

Our Client is a VC funded Fintech firm based out of Bangalore. They are building a tech based lending platform for students and young professionals, who are credit worthy but do not have access to Credit Cards and loans. The person involved in this role will evaluate alternate sources of data to model credit worthiness and risk score of applicants.

This is the most exciting time to join us as we are building our Leadership team. The leadership team will be responsible in taking the company towards our end goal of $ 1B loans, and in the process disrupt the consumer lending landscape in India.

Responsibilities

Act as Thought Leader and Subject Matter Expert in Credit Risk Analytics

Willing to get into previously unchartered territory, build completely new Risk Models, with data that has not been traditionally used for Risk Assessment.

Create, update and optimize the risk engine including business rules and scorecards, to assess creditworthiness of customers and their affordability.

Own our Predictive Analytics and Fraud Detection Engine. Use data to predict customer behavior, detect frauds.

Love data, the more the merrier. Provide insights from data analysis and statistical models, to enable lending decisions and recommend optimizations to the risk engine.

Work closely with the Technology and Business Heads, to come up with innovative risk models for our customer base.

Be the face of the company for external - Risk Function- related requirements,includes all internal and external reporting and presentation needs.

Requirements

8-12 years of total experience with MBA preferred

Prior experience of Credit Risk for Retail lending / Unsecured Loans in a financial services set-up (Banks, NBFCs or MFIs)

Experience in preparing models for Indian lending industry.

Good command over Statistical Techniques like Logistic Regression, Linear Regression, Predictive Analysis, and Decision Trees etc.

Knowledge of a variety of analytics and modeling tools and languages (R, SAS, Python) is preferred.

Exposure to machine learning techniques is desirable.