Data Scientist

RBC • Halifax, NS

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 these 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.

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 how 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.
• Primary focus will be on validation of LLM-based applications and agentic AI systems.
• May involve validating traditional machine learning models, including classification, regression, anomaly detection, natural language processing, reinforcement learning, and recommendation systems.
• 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.
• Explore ideas that interest you and build your own models and tools.
• Read research papers to enhance how our team validates models and contribute to our knowledge pool.
• Apply what you've learned to real-world problems, develop reusable software packages, and share your insights with others.
• 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