Data Scientist - Risk Analytics & Modelling

Job description

We're looking for candidates with strong analytical skills and hands-on experience working with financial datasets. You will be responsible for monitoring portfolio health, performing deep-dive exploration and analysis to identify risk drivers and improvements in our existing framework, and translating findings into actionable credit decisions. You will partner with business, product, and engineering teams to solve some of the most challenging problems in lending while also continuously improving portfolio quality by balancing risk control with growth opportunities. You will be embedded in a fast-paced environment, working within a strong team of data scientists with access to a robust data infrastructure. Responsibilities Monitor portfolio risk metrics on a regular basis and proactively flag anomalies or emerging trends Perform root cause analysis when risk indicators deteriorate to pinpoint what's driving the change, such as identifying suspicious behavioral patterns Translate findings into concrete recommendations and implement adjustments as needed Build and maintain SQL-based features, analyses, and monitoring dashboards to support ongoing risk surveillance Perform occasional model evaluation to support credit scoring improvements Fresh Graduate/Bachelor's 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 Python, SQL, and MS Excel 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 exploring data, investigating anomalies, and building SQL-based analyses Comfortable working with large tabular datasets to detect trends and anomalies, and communicating findings as actionable recommendations. Exposure to credit scoring modelling concepts is a plus.