Staff Data Scientist, Global Cyber Security & ML

rbc; Toronto; On-site; GBP 59,000 - 91,000; Full time

Benefits offered by this job

Bonuses

Flexible benefits

Stock options

Coaching & growth

Work-life balance

Challenging work

Job summary

RBC is seeking a Staff Data Scientist to lead ML model development for cybersecurity and fraud domains. You will build end‑to‑end data pipelines, mentor junior data scientists, and partner with cross‑functional teams to translate insights into actionable protections for clients and data.

The role demands advanced degrees in quantitative fields and strong Python/PySpark skills, with experience deploying complex ML systems and adapting research into production.

Qualifications

• Advanced degree in a quantitative field with strong theory.
• Proven expertise deploying ML models, including deep learning architectures.
• Strong Python and PySpark programming and data mining skills.
• Ability to translate complex concepts to technical and non‑technical audiences.

Responsibilities

• Lead ML model development and deployment to detect cyber threats and fraud.
• Design end‑to‑end data pipelines from data cleaning to production code.
• Manage complex data science projects from inception to delivery.
• Learn new methods and tools to apply research advances in production.
• Collaborate with business units to deploy scalable data products.

Skills

Advanced degree

ML model deployment

Python

PySpark

Statistical modeling

Education

Advanced degree in quantitative field

Tools

Big data technologies

Cloud platforms (AWS/Azure/GCP)

Job description

RBC is seeking a Staff Data Scientist to lead ML model development for cybersecurity and fraud domains. You will build end‑to‑end data pipelines, mentor junior data scientists, and partner with cross‑functional teams to translate insights into actionable protections for clients and data.

The role demands advanced degrees in quantitative fields and strong Python/PySpark skills, with experience deploying complex ML systems and adapting research into production.