Engineering Manager, Data Science

📋 Description

• Ship production ML systems: lead design and delivery of recommender engines, churn models, and messaging infrastructure.
• Own outcomes end-to-end: define model success, track performance, iterate to move business metrics.
• Build and develop the team: hire data scientists, coach, and maintain high expectations.
• Partner across the business: work with R&D, Finance, and GTM to scope high-leverage problems and ensure adoption.
• Set technical direction: decide tooling, architecture, and methodologies balancing delivery and maintainability.

🎯 Requirements

• Deep ML experience: 6+ years building/ deploying consumer ML systems; shipped models in production at scale.
• Leadership experience: 2+ years leading or formally managing data scientists or ML engineers.
• Technical fluency: strong Python; experience with Databricks or comparable ML platforms; full lifecycle.
• Business orientation: translate ambiguous problems into measurable ML solutions; track metrics.
• Pragmatic delivery mindset: ship MVP when needed; balance robustness with scope.
• Outcome-oriented and experimental with AI-driven development practices: actively incorporate AI tools.

🎁 Benefits

• Equal Opportunity: accommodations available; contact us to request accommodation.