Data Scientist, AI Model Risk

RBC • Toronto, ON

1d • Onsite

About The Position

RBC is a global leader in applying Artificial Intelligence (AI) in the banking sector. The AI validation team within RBC's Enterprise Model Risk Management is tasked with assessing and managing the model risk that may arise from AI capabilities. The team uses machine learning, statistical, and computational strategies to assess model risk, identifying model weaknesses early and enhancing the reliability of production models across all lines of business. This role focuses on the validation of LLM-based applications and agentic AI systems, and may also involve validating traditional machine learning models.

Requirements

• 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; comfortable with writing research experiments and willing to learn to write clean code.
• Familiarity with popular LLMs and agentic frameworks.

Nice To Haves

• A risk-oriented mindset: curious 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

Responsibilities

• 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.
• Validate LLM-based applications and agentic AI systems, and potentially traditional machine learning models (classification, regression, anomaly detection, NLP, reinforcement learning, recommendation systems).
• Challenge models and identify risks associated with their use, exploring conceptual soundness, metric reproducibility & stability, benchmarking, uncertainty quantification, fairness, privacy, explainability, and implementation controls.
• Explore ideas, build models and tools.
• Read research papers to enhance model validation techniques and contribute to the team's knowledge pool.
• Apply learned concepts to real-world problems, develop reusable software packages, and share insights.
• Collaborate with cross-functional stakeholders to establish and promote best-practices related to MLOps, tooling, and IT infrastructure.

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level