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
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.
Experience with experimentation platforms or causal inference methods
Background in subscription/SaaS businesses with retention and conversion challenges
Familiarity with TypeScript or production engineering practices
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Nagarro
Integrated DNA Technologies
Cloudera
Grafana Labs
Swiftly, Inc.
MURAL
MURAL
MURAL