Rbc
banking
DataScientist,AIModelRisk
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Scientist, AI Model Risk at Rbc. Skills: AI Model Risk, machine learning, statistical, computational strategies, model validation, risk assessment. overseeing, assessing, and managing the model risk that may arise from these AI capabilities. design and execute validation frameworks”
What You'll Achieve.
create value for our clients; enhance the reliability of production models across all lines of business; make a difference and lasting impact; achieve success that is mutual
Industry & Context.
Critical Thinking; Group Problem Solving
What They're Looking For.
Must Have
Proficient programming skills in Python or a similar, Familiarity with popular machine learning frameworks and libraries, Progress towards a PhD or Master’s degree in Statistics, Computer Science, Applied Mathematics, Econometrics, Engineering, Quantitative Finance, or a related quantitative field, communication and interpersonal skills
Nice to Have
A risk-oriented mindset, Publication or prior research experience (applied or fundamental), Experience with version control systems, Comfortable with command line tools, Familiarity with popular LLMs
What You'll Do.
and managing the model risk that may arise from these AI capabilities
design and execute validation frameworks
explore modelling considerations such as conceptual soundness
metric reproducibility & stability
uncertainty quantification
implementation controls and more
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
share your 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 and business stakeholders to inventory applications of AI and machine learning at the bank
determine their materiality
and assess whether they require review
How You'll Work.
Team & Collaboration
collaborate with cross-functional stakeholders; work with model developers (data scientists, researchers, engineers) and business stakeholders; collaborate with one another to deliver trusted advice; Work in a dynamic, collaborative, progressive, and high-performing team; collaborate effectively
Communication Scope
communication and interpersonal skills; Communication
Full Job Description
**_Job Description_** **What is the opportunity?** RBC is a global leader in applying Artificial Intelligence (AI) in the banking sector in order to create value for our 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. A failure to effectively prepare for and manage emerging model risk related to AI would subject RBC to financial, regulatory, and reputational risks and, as a result, RBC would not be able to provide its clients with the best quality service. Therefore, 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 AI validation team uses machine learning, statistical, and computational strategies to assess model risk. In doing so, RBC is able to identify model weaknesses early and enhance the reliability of production models across all lines of business. **What will you do?** * **Application** : You will have the opportunity to work in any of the many areas we work in, across an even wider 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_ * **Types of Models** Classification, regression, anomaly detection, natural language processing, computer vision, reinforcement learning, recommendation systems, dimensionality reduction, Large Language Models including generative and agentic AI * **Validation** : Your role is to challenge models and identify risks associated with their use – both conceptually and empirically. To that end, you will design and execute validation frameworks, exploring modelling considerations such as conceptual soundness, data processing, metric reproducibility & stability, be
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