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About
CANADA — TORONTO / VANCOUVER / MONTREAL
CAD $200,000 – $240,000 BASE + BONUS + BENEFITS
The Opportunity
We're partnering with a growing SaaS company at a pivotal moment in its AI evolution.
They've leveraged traditional machine learning in the past — but are now formalizing a company-wide AI strategy, with recommendation systems and customer-facing ML at the center of the roadmap.
This is about building and scaling production-grade ML systems that directly impact personalization, engagement, and revenue. You won't inherit a mature data science organization,
you'll build it.
What You'll Be Working On
Build and scale customer-facing recommendation systems in production
Design, develop, and deploy ML models across personalization, segmentation, churn prediction, and predictive analytics
Architect scalable ML systems across cloud platforms (AWS/GCP/Azure)
Partner cross-functionally with product, engineering, and leadership to translate business problems into end-to-end ML solutions
Lead and grow a small team of data scientists (starting with 1 senior DS, hiring 2 more)
Define data science operating models, standards, and best practices
Support the evolution from traditional ML to modern GenAI applications (LLMs, embeddings, RAG, etc.)
Stay hands-on (~40%) while shaping long-term AI strategy
What We're Looking For
8+ years of experience in machine learning / data science
Experience building and scaling recommendation systems in production
Proven track record deploying ML models that serve real users
Experience leading and mentoring small data science teams
Strong Python and SQL skills
Cloud ML platform experience (AWS, GCP, Azure, Databricks)
Deep understanding of supervised/unsupervised learning, experimentation, and model evaluation
Comfort operating in ambiguity and building from scratch
Strong communication skills across technical and non-technical audiences
Tech Stack
Python (NumPy, Pandas, scikit-learn)
SQL
Cloud ML platforms (AWS / GCP / Azure)
Databricks (preferred)
Big data frameworks (Spark, Hadoop)
LLMs, embeddings, and vector search
Modern MLOps and production deployment patterns
Why This Role
You'll build and scale an AI function from the ground up — not just contribute to one.
This is a true player-coach opportunity where you:
Own meaningful ML systems that shape customer experience
Define how data science operates across the company
Hire and mentor a high-impact team
Influence the AI roadmap at a strategic level
Ship production systems — not prototypes
If you're looking for a role where you can combine deep technical ownership with team leadership and long-term AI strategy, this is that opportunity.
Languages
- English
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