Description
<p><b>Data Science Manager</b></p><p><b>Location:</b> Canada | EST Hours Required </p><p><b>Salary: </b>$175-220k base + bonus </p><p><br></p><p>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.</p><p><br></p><p>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.</p><p><br></p><p>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.</p><p><br></p><p>It’s startup-level ownership with the scale and data of a large, active user base.</p><p><br></p><p><b>What You’ll Do</b></p><ul><li>Design and ship recommendation engines, churn models, and experimentation infrastructure, staying hands-on in code as the team scales</li><li>Define success metrics, monitor production models, and iterate until business results improve</li><li>Hire, coach, and develop data scientists; set a high bar for ownership, craft, and impact</li><li>Partner closely with Product, R&D, Finance, and GTM to identify high-leverage problems and deliver adopted solutions</li><li>Make pragmatic decisions around tooling, architecture, and methodology, balancing speed with long-term maintainability</li></ul><p><br></p><p><b>What We’re Looking For</b></p><ul><li>6+ years building and deploying consumer-facing ML systems in production</li><li>2+ years leading or managing data scientists or ML engineers</li><li>Experience building teams, not just operating as an IC</li><li>Strong Python skills</li><li>Experience with Databricks or similar ML platforms</li><li>Comfort across the full ML lifecycle: experimentation, feature engineering, training, deployment, monitoring</li><li>Proven ability to translate ambiguous business problems into measurable ML outcomes</li><li>Strong bias toward shipping, iteration, and impact</li><li>Sound judgment on when to ship an MVP vs. invest in robustness</li><li>Actively uses AI tools to accelerate development and expects the same from their team</li></ul><p><br></p><p><b>Nice to Have</b></p><ul><li>Experience with experimentation platforms or causal inference</li><li>Background in subscription or SaaS businesses</li><li>Familiarity with TypeScript or production engineering practices</li></ul><p></p>
