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Lead Quantitative Risk Analyst, Advanced Analytics & Credit Modelling - RBC
Software Engineer
Lead Quantitative Risk Analyst driving advanced analytics and credit modelling, building machine‑learning loss forecasts and credit scoring models while extracting, cleaning, and validating large data sets to support commercial and wholesale credit risk decisions.
About the role
Key Responsibilities
- Design, develop, and maintain machine‑learning and credit‑scoring models for Stage 3 PCL loss forecasting and revenue optimization.
- Collaborate with model development teams and credit risk experts to translate business requirements into analytical solutions.
- Extract, clean, validate, and transform large‑scale credit and client data using SQL and Python.
- Perform statistical analysis to identify drivers of credit performance, client behavior, and coverage trends.
- Communicate model insights, risk metrics, and recommendations to senior stakeholders through clear visualisations and reports.
Requirements
- Advanced degree (MSc/PhD) in Quantitative Finance, Statistics, Computer Science, or related field.
- 5+ years of experience building predictive models in credit risk, including machine‑learning and statistical techniques.
- Proficiency in Python (pandas, scikit‑learn) and SQL for data manipulation and model implementation.
- Strong understanding of credit scoring, loss forecasting, and regulatory frameworks (e.g., IFRS 9, Basel).
- Excellent analytical communication skills, with the ability to present complex findings to non‑technical audiences.
Skills
machine learningpythonsql