Data Scientist - Risk Platform

Data Scientist We’re looking for fresh graduates with experience in manipulating datasets and building statistical models for credit risk by leveraging machine learning techniques. You will partner with business, product, and engineering teams to explore, determine, analyze, propose, and solve some of the most challenging business problems in lending and reduce risk. You will perform deep-dive exploration and analysis to find improvements in our existing framework. You will be submerged in a fast-paced environment working tightly in a strong team of data scientists and experience firsthand our robust data infrastructure.

Responsibilities

Perform features selection, parameter binning, and optimize custom predictive machine learning models for credit risk scoring

Query, process, cleanse, and verify the integrity of data used for analysis

Analyze key metrics to determine risk and explore areas for improvements

Create reports and dashboards to monitor model impact and performance

Help build the variety of data ingredients needed to do modelling effectively

Maintain the credit risk model platform which is utilized by the team

Provide advice and guidance on potential efficiency gains and new state-of-the-art credit risk modelling methodologies

Qualifications

Bachelor degree in an analytical or quantitative discipline (e.g. math, statistics, engineering, computer science), however other disciplines will be considered

Experienced in using statistical computer languages such as R, Python, SAS, SQL, or advanced MS Excel skills is a plus.

Have good communication skills and able to work together in a team

Excellent problem-solving skills and have the drive to learn and master new technologies and techniques

Willingness to learn new skills independently and have a strong sense of project ownership

Not afraid to get your hands dirty to explore data and build statistical models

Seniority Level Associate

Employment Type Full-time

Job Function Engineering and Information Technology

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