Senior Data Scientist (m/f/d)

AutoTrader.ca•Toronto, ON

32d

About The Position

We are TRADER, a Canadian leader in digital automotive solutions. Our flagship brands - AutoTrader.ca, AutoSync, Dealertrack Canada and CMS - help Canadians buy, sell, and finance vehicles with confidence. Learn more at tradercorporation.com. As part of AutoScout24 group, Europe’s largest online car marketplace, we’re shaping the future of automotive retail in Canada and beyond. As Senior Data Scientist, you will be key contributor within our global Data Science organization, shaping AI and machine learning systems that sit at the core of our marketplace platforms across Europe and Canada. You design, build, and operate machine learning and GenAI systems that run in production and matter to the business. You work on complex, ambiguous problems, exercise strong technical judgment, and take responsibility for outcomes - not just models. While this is a hands-on role, you are also expected to influence technical direction, raise the bar for engineering practices, and enable other data scientists and engineers to succeed. This position is permanent and based in Toronto.

Requirements

Advanced degree in Computer Science, Engineering, Mathematics, or a comparable field
5+ years of experience building and owning production ML systems end-to-end in a real-world environment
Strong hands-on expertise in Python and modern ML frameworks, with the ability to ship, operate, and evolve ML systems in production
Solid experience with AWS and MLOps practices, including deployment, monitoring, and operating ML systems at scale
Practical experience applying GenAI / LLMs in applied or production contexts, with a clear understanding of trade-offs and limitations
Ability to influence technical decisions through clear judgment and collaboration, not formal authority
Experience supporting and guiding more junior data scientists through hands-on collaboration, code reviews, and technical feedback
Strong sense of ownership and accountability for outcomes, with a pragmatic approach to trade-offs and delivery

Responsibilities

Own ML and GenAI systems end-to-end in production, including problem framing, system design, deployment, monitoring, and continuous improvement
Make sound architectural and methodological decisions for ML systems on AWS, balancing robustness, observability, cost, and long-term maintainability
Partner closely with Product, Engineering, and Business leaders to turn ambiguous problems into ML-enabled product solutions with clear outcomes
Operate and evolve ML systems deployed across multiple countries and markets, accounting for differences in data distributions, regulations, and constraints
Mentor other data scientists through hands-on technical guidance, design reviews, and shared ownership of systems