About this role
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 challenging business problems in lending and reduce risk. Perform deep-dive exploration and analysis to find improvements in existing frameworks.
Query, process, cleanse, and verify the integrity of data used for analysis. Perform feature selection, parameter binning, and optimize custom predictive machine learning models for credit risk scoring. Analyze key metrics to determine risk and explore areas for improvements.
You will be submerged in a fast-paced environment working tightly with a strong team of data scientists. Experience firsthand our robust data infrastructure. Create reports and dashboards to monitor model impact and performance.
Help build the variety of data ingredients needed to do modeling effectively. Maintain the credit risk model platform utilized by the team. Provide advice and guidance on potential efficiency gains and new state-of-the-art credit risk modeling methodologies.
Requirements
- Bachelor degree in an analytical or quantitative discipline (e.g. math, statistics, engineering, computer science); other disciplines considered
- Experienced in using statistical computer languages such as R, Python, SAS, SQL, or advanced MS Excel skills
- Excellent problem-solving skills and drive to learn and master new technologies and techniques
- Willingness to learn new skills independently and strong sense of project ownership
- Not afraid to get hands dirty to explore data and build statistical models
Responsibilities
- Perform feature 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 modeling effectively
- Maintain the credit risk model platform utilized by the team
- Provide advice and guidance on potential efficiency gains and new state-of-the-art credit risk modeling methodologies
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