Analyst, Credit Risk Scoring

The Analyst, Credit Risk Scoring supports the development, monitoring, validation, and optimization of credit risk, fraud, and account management scorecards used throughout the customer lifecycle. Through portfolio analytics, model performance monitoring, customer segmentation, and statistical analysis, the analyst helps generate insights and recommendations that support scorecard strategy, model governance, risk management, and business decision-making. The role provides exposure to predictive analytics, credit scoring methodologies, machine learning concepts, and decision science within a regulated financial services environment.

Analyze customer, portfolio, bureau, and scorecard performance data to identify emerging trends, opportunities, and risk signals across acquisition, account management, collections, and fraud strategies.

Support the development, monitoring, recalibration, and optimization of scorecards and alternative risk frameworks through quantitative analysis and performance evaluation.

Produce recurring score monitoring reports and dashboards, including score stability, predictive power, segmentation performance, portfolio outcomes, and key risk indicators.

Monitor model and scorecard performance using industry-standard metrics such as GINI, KS, AUC, PSI, bad rates, approval rates, and portfolio profitability measures.

Support the investigation of score performance shifts, model drift, data anomalies, and emerging portfolio trends.

Develop and maintain SQL queries, analytical datasets, automated reporting solutions, and dashboards to improve monitoring efficiency and governance processes.

Apply statistical techniques, machine learning concepts, and data visualization tools to generate actionable business insights.

Prepare presentations and analytical summaries supporting score strategy reviews, model governance forums, and management discussions.

Collaborate with stakeholders across Credit Risk, Fraud, AML, Product, Marketing, Technology, and Model Risk Management.

Contribute to continuous improvement initiatives related to model monitoring, reporting automation, data quality, and analytical processes.

• Bachelor's degree in quantitative discipline such as Statistics, Mathematics, Engineering, Computer Science, Data Science, Physics, Economics, or another STEM-related field.
• 0 to 5 years of experience in data analytics, business analytics, risk analytics, financial services, consulting, or a related quantitative field. New graduates with strong technical and analytical capabilities are encouraged to apply.
• Strong technical proficiency in SQL and Python, with demonstrated ability to manipulate, analyze, and interpret large datasets.
• Strong analytical and problem-solving skills with the ability to translate data into meaningful insights and recommendations.
• Excellent attention to detail and commitment to data quality and accuracy.
• Effective written and verbal communication skills, with the ability to explain analytical findings to both technical and non-technical audiences.
• Demonstrated ability to learn quickly, adapt to changing priorities, and work effectively in a fast-paced environment.
• Experience with data visualization tools, cloud-based analytics platforms, machine learning, or statistical modelling is considered an asset.
• Strong Microsoft Office skills, particularly Excel and PowerPoint.
• Curiosity, initiative, and a willingness to develop expertise in credit risk, fraud prevention, customer acquisition, and financial services analytics.