RBC is a global leader in applying Artificial Intelligence in the banking sector to create value for clients. The Data Scientist, AI Model Risk role supports the AI validation team by assessing model risks in LLM-based applications, agentic AI systems, and traditional machine learning models, while conducting research and promoting MLOps and infrastructure best practices.
**Application**: You will have the opportunity to collaborate with teams across a wide variety of business functions, such as the following: *Internal Audit, Cybersecurity, Fraud Management, Anti-Money Laundering, Insurance, Credit Risk, Technology Operations, Identity & Access Management, Human Resources.*
**Technical Scope**: Your primary focus will be on validation of LLM-based applications and agentic AI systems. This role may also involve validating traditional machine learning models, including classification, regression, anomaly detection, natural language processing, reinforcement learning, and recommendation systems
**Validation**: Your role is to challenge models and identify risks associated with their use – both conceptually and empirically. To that end, you will explore modelling considerations such as conceptual soundness, metric reproducibility & stability, benchmarking, uncertainty quantification, fairness, privacy, explainability, implementation controls and more. You will also have the freedom to explore ideas that interest you and build your own models and tools
**Research & Development**: You will read research papers (established work and state-of-the-art) to enhance how our team validates models and contribute to our knowledge pool. You are encouraged to apply what you've learned to real-world problems, develop reusable software packages, and share your insights with others
**IT**: You will collaborate with cross-functional stakeholders to establish and promote best-practices related to MLOps, tooling and IT infrastructure
Qualification
Required
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; you should already be comfortable with writing research experiments and be willing to learn how to write clean code
Familiarity with popular LLMs and agentic frameworks
Preferred
A risk-oriented mindset: You are 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
Benefit
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
Royal Bank of Canada is a global financial institution with a purpose-driven, principles-led approach to delivering leading performance.
