Lead Quantitative Risk Analyst, Advanced Analytics & Credit Modelling
Royal Bank of Canada Voir toutes les offres
- Toronto, ON
- Permanent
- Temps-plein
- Modeling responsibilities covers a wide variety of models including, but not limited to, Machine learning, Artificial Intelligence, credit scoring, and surveillance models for the purpose of reducing losses or driving revenue in the portfolio
- Responsible for sourcing data, engineering features, as well as developing, monitoring, and deploying models.
- Extract, clean, validate, and analyze usable data from multiple data sources/providers to quantify borrower behavioral patterns.
- Participate in data assessment and procurement for credit modeling and analytics, help automate the underlying credit modeling feature farm, assess, and address data gaps, as well as develop, monitor, and deploy credit risk models.
- Engage with stakeholders and experts across adjudication and line-of-business throughout model development cycle; solicit input from expert and ensure models are business-sound.
- Prepare model documentation, source codes, presentation decks, and/or model monitoring reports.
- Responsible for resolving issues raised by independent validation, Internal Audit and ongoing model monitoring.
- Undergraduate degree in computer science, finance, mathematics, statistics, or economics, with at least 5 years of working experience in related credit risk modeling roles.
- Hands-on experiences with large datasets (ingestion, processing, merging and aggregation of data), with fluency in both SQL and big data/cloud technologies (Hadoop, PySpark, S3).
- Strong Python coding skills to support automation and efficient end-to-end model scoring/implementation.
- Strong understanding and working knowledge of advanced statistical methods and machine learning techniques for classification and regression tasks.
- Demonstrated knowledge of credit risk models and time series analysis.
- Experience in code sharing and version control solutions (GitHub).
- Master degree in computer science, finance, mathematics, statistics, or economics.
- Knowledge of GenAI use cases in retail / commercial lending.
- Knowledge of other programming languages such as R, Java, Scala, or SAS.
- Ability to work with UNIX command line.
- Prior model development experiences for IFRS9, stress testing or capital measurement.
- Ability to make a difference and lasting impact
- Work in a dynamic, collaborative, progressive, and high-performing team
- Opportunities to do challenging work