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
Your role is to challenge models and identify risks associated with their use – both conceptually and empirically.
You will design and execute validation frameworks, exploring modelling considerations such as conceptual soundness, data processing, metric reproducibility & stability, benchmarking, robustness, 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.
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.
You will collaborate with cross-functional stakeholders to establish and promote best-practices related to MLOps, tooling and IT infrastructure.
You will 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.

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; you should already be comfortable with writing research experiments and be willing to learn how to write clean code.
Familiarity with popular machine learning frameworks and libraries
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 LLMs

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

Data Scientist, AI Model Risk

Key skills