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Senior Data Scientist
Zoom
Remote, IND
Full-time
Senior · 8+ years experience
Remote
Discovered 3 weeks ago
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What you can expect
This role involves engineering Zoom's Revenue Intelligence Engine to transform raw telemetry into actionable insights that drive customer growth, retention, and expansion. Responsibilities include building scalable systems for lead scoring, expansion modeling, and churn prediction within a multi-product SaaS environment. Tasks span data exploration, model development, deployment, monitoring, and strategic storytelling. Collaboration with Product and Data Engineering ensures telemetry contracts, reusable analytics frameworks, and production-ready models. Efforts include investigating conversion issues and presenting findings to executives. The work directly influences revenue by supporting Sales, Product, and CS teams with data-driven decision-making tools.
About the Team
This role sits within the Revenue Intelligence team, focused on building the data foundation and ML systems that drive Zoom’s go-to-market strategy. The team partners with Sales, Product, Customer Success, and Data Engineering to ensure that the right signals reach the right people at the right time — from PQL scores surfaced in the GTM stack to experimentation insights delivered to Product leadership.
Responsibilities
- Designing telemetry models to predict user journey transitions and support growth initiatives.
- Conducting exploratory data analysis and diagnosing trends like conversion drops or churn spikes.
- Managing the MLOps lifecycle, including retraining pipelines and model registry oversight.
- Defining batch and real-time inference strategies for model serving architecture.
- Maintaining dashboards, alerts, and incident response protocols for model performance.
- Establishing data validation frameworks and ensuring schema and data quality standards.
- Develop analytics design systems and own experimentation — standardize metric definitions across products, build modular dbt data models, and own the full A/B testing lifecycle from design to ship/no-ship recommendations
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