Data Scientist, AI Model Risk

RBC is a global leader in applying Artificial Intelligence (AI) in the banking sector in order to create value for our clients, with capabilities ranging from LLM-powered digital banking to voice assistants in customer service. A failure to effectively prepare for and manage emerging model risk related to AI would subject RBC to financial, regulatory, and reputational risks and, as a result, RBC would not be able to provide its clients with the best quality service. Therefore, the AI validation team within RBC's Enterprise Model Risk Management is tasked with assessing and managing the model risk that may arise from these AI capabilities.

What will you do?

• Application: Collaborate with teams across a wide variety of business functions, such as Internal Audit, Cybersecurity, Fraud Management, Anti-Money Laundering, Insurance, Credit Risk, Technology Operations, Identity & Access Management, Human Resources.
• Technical Scope: Focus on validation of LLM-based applications and agentic AI systems. The role may also involve validating traditional machine learning models, including classification, regression, anomaly detection, natural language processing, reinforcement learning, and recommendation systems.
• Validation: Challenge models and identify risks associated with their use – both conceptually and empirically. Explore modelling considerations such as conceptual soundness, metric reproducibility & stability, benchmarking, uncertainty quantification, fairness, privacy, explainability, implementation controls and more. Build your own models and tools.
• Research & Development: Read research papers to enhance our validation methods and contribute to the knowledge pool. Apply what you learn to real-world problems and develop reusable software packages.
• IT: Collaborate with cross-functional stakeholders to establish and promote best practices related to MLOps, tooling and IT infrastructure.

What do you need to succeed?

Must-have

• Passionate about learning and staying up-to-date with research and technology.
• Strong communication and interpersonal skills.
• Progress towards a PhD or Master’s degree in Statistics, Computer Science, Applied Mathematics, Econometrics, Engineering, Quantitative Finance, or a related quantitative field.
• Proficient programming skills in Python and comfortable writing research experiments.
• Familiarity with popular LLMs and agentic frameworks.

Nice-to-have

• Risk-oriented mindset and curiosity about the "how" as well as the "why".
• Publication or prior research experience (applied or fundamental).
• Experience with version control systems.
• Comfortable with command line tools.
• Familiarity with popular machine learning frameworks and libraries.

What's in it for you?

• Work in a dynamic, collaborative, progressive, and high-performing team.
• Opportunities to do challenging work, make a difference and lasting impact.
• Continuous learning and flexibility to work on projects that you are passionate about.
• Leaders who support your development through coaching and managing opportunities.

RBC values an inclusive workplace with diverse perspectives. We believe that a supportive environment drives collaboration, innovation, and professional growth.