Job Description 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 team 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 scoringQuery, process, cleanse, and verify the integrity of data used for analysisAnalyze key metrics to determine risk and explore areas for improvementsCreate reports and dashboards to monitor model impact and performanceHelp build the variety of data ingredients needed to do modelling effectivelyMaintain the credit risk model platform which is utilized by the teamProvide 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 consideredExperienced 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 teamExcellent problem-solving skills and have the drive to learn and master new technologies and techniquesWillingness to learn new skills independently and have a strong sense of project ownershipNot afraid to get your hands dirty to explore data and build statistical models