Description

Data Science Manager

Location: Canada | EST Hours Required

Salary: $175-220k base + bonus

We’re partnering with a high-growth product company to hire a Data Science Manager to both ship production ML systems and build a high-performing team.

This is a true player-coach role: you’ll stay hands-on with modeling and system design while setting technical direction, hiring, and mentoring data scientists. The expectation is clear: deliver models that move retention, conversion, and revenue.

You’ll join a small, autonomous data science team with impact across Product, R&D, Finance, and GTM. The team builds customer-facing data products such as recommendation systems, churn models, and experimentation frameworks that influence how millions of users discover value.

It’s startup-level ownership with the scale and data of a large, active user base.

What You’ll Do

  • Design and ship recommendation engines, churn models, and experimentation infrastructure, staying hands-on in code as the team scales
  • Define success metrics, monitor production models, and iterate until business results improve
  • Hire, coach, and develop data scientists; set a high bar for ownership, craft, and impact
  • Partner closely with Product, R&D, Finance, and GTM to identify high-leverage problems and deliver adopted solutions
  • Make pragmatic decisions around tooling, architecture, and methodology, balancing speed with long-term maintainability

What We’re Looking For

  • 6+ years building and deploying consumer-facing ML systems in production
  • 2+ years leading or managing data scientists or ML engineers
  • Experience building teams, not just operating as an IC
  • Strong Python skills
  • Experience with Databricks or similar ML platforms
  • Comfort across the full ML lifecycle: experimentation, feature engineering, training, deployment, monitoring
  • Proven ability to translate ambiguous business problems into measurable ML outcomes
  • Strong bias toward shipping, iteration, and impact
  • Sound judgment on when to ship an MVP vs. invest in robustness
  • Actively uses AI tools to accelerate development and expects the same from their team

Nice to Have

  • Experience with experimentation platforms or causal inference
  • Background in subscription or SaaS businesses
  • Familiarity with TypeScript or production engineering practices