What will you do? Develop Python-based analytical tools and automation that support stress testing processes, results analysis and benchmarking. Apply generative AI and LLM tools to real risk problems — scenario narrative drafting support, document and news analysis for emerging risk signals, code generation and quality checks, and automated report production. Analyze stress testing outputs to explain drivers of projected credit losses and capital impacts, and prepare clear summaries for technical and non-technical stakeholders. Build interactive dashboards and visualizations (Python, and HTML-based) that make stress testing results easier to interrogate and consume. Contribute to prototyping work on AI-enabled risk monitoring and stress testing platforms — from gathering requirements through to testing and documentation. Participate in design sessions with quantitative analysts and downstream users, contribute ideas, document requirements and share what you learn with the team. What do you need to succeed? Must-have Currently enrolled in a post-secondary program in a quantitative discipline (e.G. Data Science, Statistics, Mathematics, Computer Science, Engineering, Finance or Economics). Working knowledge of Python, with the ability to manipulate, analyze and validate large datasets. Working knowledge of SQL and comfort querying relational data. Strong quantitative,



analytical and problem-solving skills, with careful attention to detail and data accuracy. Hands-on experience with generative AI or LLM tools, including effective prompt engineering. Strong MS Office skills, particularly Excel (advanced formulas and data manipulation), PowerPoint and Word. Intellectual curiosity and a self-starter mindset — comfortable asking questions and picking up new tools quickly. Nice-to-have Exposure to credit risk, macroeconomics or econometrics coursework, including regression, time series or forecasting techniques. Familiarity with stress testing or regulatory capital concepts (CCAR, IFRS 9, Basel). Interest in how AI agents, retrieval-augmented generation and MCP integrations can be applied to production analytics workflows. Experience with data visualization tools such as Tableau, or building HTML-based or web dashboards. Exposure to SAS, R, or large-scale enterprise data environments. Familiarity with version control and team-oriented development practices (Git). Ability to explain technical results in plain business language to non-technical partners. Strong organizational and time-management skills, with the ability to balance recurring deliverables against ad-hoc requests. What's in it for you? Direct exposure to how a global

📌 2027 Winter - Grm, Ai & Stress Testing Analytics Intern (4 Months) (Toronto)
🏢 0000050007 Royal Bank of Canada
📍 Toronto