Data Science Manager (Recommendation Systems)

Data Science Manager (Recommendation Systems)

Remote (Canada)

$180,000 – $240,000 CAD base + equity + benefits

About the Company

This organization builds widely used collaborative software that helps distributed teams plan, design, and execute complex work together. The platform supports millions of users who rely on it to organize ideas, coordinate projects, and make decisions more effectively.

Engineering teams operate in small, autonomous groups that own meaningful product outcomes. The culture emphasizes practical problem solving, thoughtful experimentation, and systems that deliver measurable value to users.

Data science plays a central role in shaping how users discover content, navigate workflows, and extract value from the platform. Machine learning systems directly influence user engagement, retention, and overall product growth.

About the Role

This is a hands-on leadership role responsible for building and evolving the machine learning systems that power personalized experiences across the product.

You will lead a small team of data scientists while remaining actively involved in system design, model development, and production deployment. A primary focus of the role is the development and scaling of recommendation systems that help users discover relevant content, workflows, and collaboration opportunities.

You will partner closely with product, engineering, and business teams to identify high-impact opportunities where recommendation models and predictive systems can improve user experience and drive measurable business outcomes.

Success in this role will be defined by the performance of deployed systems — particularly recommendation models that improve discovery, engagement, and retention across a large user base.

Key Responsibilities

• Design and deploy production recommendation systems that personalize product experiences for users
• Lead development of machine learning models that improve content discovery, engagement, and user retention
• Architect scalable pipelines for feature generation, model training, evaluation, and deployment
• Establish clear success metrics for recommendation models and continuously improve system performance
• Hire, mentor, and develop a small team of data scientists focused on applied product impact
• Partner with product and engineering teams to identify opportunities where recommendation systems can drive measurable improvements in user experience
• Build experimentation frameworks to evaluate ranking strategies and recommendation effectiveness
• Guide the evolution of recommendation architecture as the product and user base continue to scale

Must Haves

• 6+ years building and deploying machine learning systems in production environments
• 2+ years leading or managing data scientists or machine learning engineers
• Demonstrated experience developing recommendation systems at scale
• Strong Python expertise and experience building production ML pipelines
• Experience with ranking models, collaborative filtering, or similar recommendation techniques
• Experience translating product or business problems into measurable ML solutions

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• Strong understanding of the full ML lifecycle including feature engineering, training, deployment, and monitoring

Nice to Have

• Experience designing experimentation platforms or evaluating ranking algorithms
• Background in user behavior modeling, personalization, or search ranking
• Experience working with large-scale product datasets or distributed data platforms
• Familiarity with production backend systems or modern software engineering practices

Why Join

• Own the recommendation systems that shape how users discover and interact with the product
• Lead a high-impact data science team solving complex personalization challenges
• Work closely with product and engineering leaders to shape the future of machine learning within the platform
• Opportunity to influence systems used by millions of users globally
• Competitive compensation, equity participation, and comprehensive benefits
• Remote-first culture across Canada with strong technical standards and a collaborative engineering environment