About The Position

The Retail Risk Modeling team specializes in leveraging large datasets to generate new insights and make fact-based decisions on how to profitably grow loan originations by balancing risk, pricing, operational efficiency, and customer impact. As a co-op analyst on the team, you will support the team on analyzing, implementing, and testing solutions to support real-time decision making for RBC’s lending business lines (credit cards, home equity financing, various unsecured loans, automotive financing, small business). You will have the opportunity to develop deep understanding of all of RBC's retail banking product offerings through advanced data analytics.

Requirements

  • Currently enrolled in a related degree in computer science, finance, mathematics, statistics, or engineering.
  • Hands-on SQL and Python coding skills.
  • Understanding of advanced statistical methods, machine learning, and AI techniques for classification and regression tasks.
  • Demonstrated capability with AI tools such as copilot

Nice To Haves

  • Experiences with large datasets and big data/cloud technologies (Hadoop, PySpark, S3).
  • Experience in code sharing and version control solutions (GitHub).
  • Ability to work with UNIX command line.

Responsibilities

  • Support in developing and maintaining AI and machine learning credit risk models used in the credit decisions related to RBC’s various lending business lines across all risk spectrums (from prime to subprime).
  • Extract, clean, validate, and analyze usable data from multiple data sources/providers in a distributed compute environment to quantify borrower behavioral patterns and market dynamics.
  • Support the construction of prediction systems through the usage of machine learning tools and advanced statistics to select features, create and optimize classifiers or regression models.
  • Present result in a clear and concise manner for non-technical stakeholders and comprehensive model documentation.
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