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About the Role

RBC is a global leader in applying AI in the banking sector to create value for clients. The AI validation team oversees, assesses, and manages model risk arising from AI capabilities using machine learning, statistical, and computational strategies to identify model weaknesses and enhance reliability.

Responsibilities

• Design and execute validation frameworks to challenge models and identify risks.
• Explore modelling considerations such as conceptual soundness, data processing, metric reproducibility & stability, benchmarking, robustness, uncertainty quantification, fairness, privacy, explainability, and implementation controls.
• Explore ideas and build own models and tools.
• Read research papers to enhance model validation 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.
• Work with model developers and business stakeholders to inventory AI/ML applications, determine materiality, and assess review requirements.

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 or a similar language.
• Comfortable with writing research experiments.
• Willing to learn how to write clean code.
• Familiarity with popular machine learning frameworks and libraries.
• 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.

Skills

• Client Counseling
• Communication
• Critical Thinking
• Financial Instruments
• Group Problem Solving
• Investment Risk Management
• Market Risk
• Quantitative Methods
• Risk Management
• Classification
• Regression
• Anomaly Detection
• Natural Language Processing
• Computer Vision
• Reinforcement Learning
• Recommendation Systems
• Dimensionality Reduction
• Large Language Models
• Generative AI
• MLOps

Location

• ROYAL BANK PLAZA, 200 BAY ST:TORONTO
• Toronto
• Canada

Work Type

• Full time

Education Level

• PhD or Master’s degree in Statistics, Computer Science, Applied Mathematics, Econometrics, Engineering, Quantitative Finance, or a related quantitative field

Salary/Compensations

• Salaried

About the Company

• RBC is a global financial institution with a purpose-driven, principles-led approach to delivering leading performance.
• Success comes from the 84,000+ employees who bring RBC's vision, values and strategy to life.
• As Canada’s biggest bank, and one of the largest in the world based on market capitalization, RBC has a diversified business model with a focus on innovation and providing exceptional experiences to more than 16 million clients in Canada, the U.S. and 34 other countries.
• RBC supports a broad range of community initiatives through donations, community investments and employee volunteer activities.

Equal Opportunity

• At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC.
• We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world.
• Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities.
• RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.