Data Science Manager, MURAL

• Deep ML experience: 6+ years building and deploying consumer-facing ML systems—recommendation engines, churn models, or similar. You've shipped models that ran in production at scale, not just notebooks
• Leadership experience: 2+ years leading or formally managing data scientists or ML engineers. You've built teams, not just participated in them
• Technical fluency: Strong Python skills; experience with Databricks or comparable ML platforms. Comfortable across the full lifecycle—experimentation, feature engineering, model training, deployment, monitoring
• Business orientation: Track record of translating ambiguous business problems into measurable ML solutions. You care whether the model moved the metric, not just whether it trained
• Pragmatic delivery mindset: You know when to ship an MVP to get feedback and when to invest in robustness. You edit scope ruthlessly rather than letting projects bloat
• An outcome-oriented and highly experimental interest in AI-driven development practices: You actively incorporate AI tools into your workflow and expect the same from your team

Desirable

• The Data Science team builds the predictive engines and analytical capabilities that power decisions across Mural. We're a small team within the Data Organization, delivering data products—recommendation systems, churn models, experimentation frameworks—to R&D, Finance, and GTM
• Our work directly influences how millions of users discover value in Mural and how the business grows. We operate with the autonomy of a small team and the reach of a company with a large, active user base
• As Data Science Manager, you will own the delivery and evolution of Mural's data products while building a high-performing team
• This is a player-coach role—you'll stay hands-on with model development and system design while setting technical direction and growing your team's capabilities
• You will partner closely with R&D, Finance, and GTM stakeholders to turn business problems into deployed models that move metrics
• Your success will be measured by whether the models you ship actually improve retention, conversion, and revenue—not by the sophistication of the approach
• Ship production ML systems: Lead the design and delivery of recommendation engines, churn prediction models, and messaging experimentation infrastructure—staying hands-on in code while your team scales
• Own outcomes end-to-end: Define model success criteria, track performance across all deployed models, and iterate until business metrics move—not just until models deploy
• Build and develop the team: Hire strong data scientists, coach them through technical and career challenges, and maintain high expectations for both craft and impact
• Partner across the business: Work directly with R&D, Finance, and GTM to identify high-leverage problems, scope solutions that can ship incrementally, and ensure data products get adopted—not just delivered
• Set technical direction: Make pragmatic decisions about tooling, architecture, and methodology that balance near-term delivery with long-term maintainability