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Tier-1 brand and remote posting increase applicants, but senior specialized ML/MLOps requirements limit broad competition.
Deep ML, MLOps, and product analytics expertise required, limiting cross-industry transferability.
Explicit 8+ years plus mandatory ML/MLOps, dbt, Snowflake, and production modeling create strict filters.
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Engineer and maintain Zoom's Revenue Intelligence Engine transforming telemetry data into actionable insights for growth, retention, and expansion.
Build and deploy scalable lead scoring, expansion modeling, and churn prediction models within a multi-product SaaS environment.
Manage end-to-end ML lifecycle including data exploration, model development, MLOps, monitoring, and communicating insights to executive stakeholders.
8+ years experience in product analytics or applied data science with quantitative degree (Statistics, Data Science, Computer Science, Economics, or related).
Expertise in SQL and Python for advanced querying and data modeling.
Experience building, deploying, and monitoring ML models using MLOps platforms like MLflow, SageMaker, or Vertex AI.
Proficient with telemetry/event data modeling, data transformation tools (dbt, Snowflake), and A/B testing frameworks.
Experienced in scaling production ML systems and managing model lifecycle in a fast-paced SaaS environment.
Strong business intuition and expertise in customer lifecycle, growth analytics, and conversion funnel optimization.
Skilled communicator able to present complex data-driven recommendations to VP and C-level stakeholders and mentor junior data scientists.