Data Scientist - Credit Risk

Data Scientist in the team has end-to-end responsibilities to develop and deploy machine learning models that support every function of the business - credit & fraud risk, KYC, marketing, process, pricing optimization, etc. All aspects of the business are data-driven, and all processes and standard practices are open to data-driven disruption.

Key Responsibilities

- Understand the business deeply and translate business problems to data science and analytics problems

- Understanding of regulatory landscape of lending business and Loss understanding would be a good plus. Exposure to regulatory model development/Monitoring/Reporting concepts.

- Brainstorm, research, learn & implement using new/existing AI & Machine learning algorithms

- Use data to create statistical models and build algorithms using R / Python

- Develop solid understanding of business drivers and how data is used to drive decisions and behaviors

- Collaborate with multiple stakeholders and teams such as Product, Operations, Collections etc

- Presenting model results to management using data visualization tools

- Develop, deploy, and manage scalable data pipelines, and APIs.

Desired Skills/Experience

- 1- 5 years of experience in hands-on ML model development using python/R

- Mathematical & statistical understanding of machine learning algorithms (GBM, Logistic Regression, Linear Regression, Deep Learning)

- Ability to draw conclusions and insights from data. Strong logical reasoning and data interpretation skills

- Excellent communication and interpersonal skills & the desire to solve hard problems

- Experience in model deployment is strongly preferred

- Experience in leading a team of data scientists, python developers, analysts is strongly preferred

- Background in Statistics, Mathematics, Economics, Finance and Computer Science is strongly preferred