Data Scientist

Royal Bank of Canada • Dartmouth, NS

48d • Onsite

About The Position

RBC is a global leader in applying Artificial Intelligence (AI) in the banking sector to create value for clients, with capabilities ranging from LLM-powered digital banking, boosting ensembles in fraud detection and AML, voice assistants in customer service, to algorithmic trading in capital markets. The AI validation team within RBC’s Enterprise Model Risk Management (RBC Group Risk Management) is tasked with overseeing, 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 or a similar language
• Comfortable with writing research experiments and willing to learn how to write clean code
• Familiarity with popular machine learning frameworks and libraries

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

Responsibilities

• Work in various areas and business functions related to AI models, including Classification, regression, anomaly detection, natural language processing, computer vision, reinforcement learning, recommendation systems, dimensionality reduction, and Large Language Models including generative AI.
• Apply skills across business functions such as Internal Audit, Cybersecurity, Fraud Management, Anti-Money Laundering, Insurance, Credit Risk, Technology Operations, Identity & Access Management, and Human Resources.
• Challenge models and identify risks associated with their use, both conceptually and empirically.
• Design and execute validation frameworks, exploring 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 (established work and state-of-the-art) to enhance how the team validates models and contribute to the knowledge pool.
• Apply learned knowledge to real-world problems, develop reusable software packages, and share insights with others.
• Collaborate with cross-functional stakeholders to establish and promote best-practices related to MLOps, tooling and IT infrastructure.
• Work with model developers (data scientists, researchers, engineers) and business stakeholders to inventory applications of AI and machine learning at the bank, determine their materiality, and assess whether they require review.

Benefits

• Leaders who support your development through coaching and managing opportunities
• Flexibility to work on projects that you are passionate about
• Ability to make a difference and lasting impact
• Work in a dynamic, collaborative, progressive, and high-performing team
• Opportunities to do challenging work and make a difference
• Opportunities to build close relationships
• An inclusive workplace that has diverse perspectives
• A workplace where employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Number of Employees: 101-250 employees