ML Engineer Lead – Credit Risk Production Systems

RBC; Toronto; On-site; CAD 110,000 - 160,000; Full time

Benefits offered by this job

Health insurance

Flexible work options

Job summary

RBC Centre in Toronto is seeking a Manager, ML Engineer to translate models into production-grade services that support model development within the Credit Modeling and Methodology Team. You will lead cross-functional collaboration across data scientists, risk modelers, and IT to deliver robust production code.

You will own the full lifecycle from development to deployment of ECL and stress testing models, build scalable ETL pipelines, and drive CI/CD automation while upholding rigorous QA and

Qualifications

• 0-2+ years related work experience in developing and/or deploying scalable coding solutions (including distributed systems and performance optimization)
• Knowledge of credit risk methodology, including capital estimation, credit risk/stress testing models, and/or direct experience with Expected Credit Loss implementations
• Experience in data manipulation on large datasets
• Credit Risk & Banking Stress Testing: Understanding of provisioning models and stress testing frameworks
• Testing & QA: UAT/PIV testing experience in regulated financial environments
• Experience in code management solutions (e.g. Git, GitHub)
• Strong interpersonal, communication, and presentation skills, with the ability to articulate and document complex technical problems and control process for a non-technical audience
• Ability to structure workload and manage multiple competing priorities.
• Conceptual thinker with a curious mind to challenge and question the status quo and the ability to make quantitative decisions on ambiguous and complex techniques which will have an impact directly on RBC's financial statements.

Responsibilities

• Develop credit risk model implementations
• Engineer and maintain scalable ETL pipelines and workflows
• Apply and advocate for coding best practices to optimize production system performance, scalability, and reliability
• Development of scalable testing solutions to increase coverage, including unit testing
• Build and maintain CI/CD automation (Docker, Kubernetes, OpenShift, GitHub Actions, Airflow)
• Contribute to code reviews, collaborate with data scientists, credit risk modelers, and IT teams
• Maintain technical documentation and submit change control UAT/PIV evidence for validation review
• Use of quantitative methods to diagnose model implementation issues and impacts

Skills

Python

PySpark

GitHub

Code Reviews

Communication

Critical Thinking

Financial Instruments

Financial Regulation

Quantitative Methods

Risk Management

Source Code Management

Tools

Git

GitHub

Docker

Kubernetes

OpenShift

GitHub Actions

Airflow

Job description

RBC Centre in Toronto is seeking a Manager, ML Engineer to translate models into production-grade services that support model development within the Credit Modeling and Methodology Team. You will lead cross-functional collaboration across data scientists, risk modelers, and IT to deliver robust production code.

You will own the full lifecycle from development to deployment of ECL and stress testing models, build scalable ETL pipelines, and drive CI/CD automation while upholding rigorous QA and