Impress employers and recruiters.
Choose from hundreds of resume examples.

Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

Build production machine-learning systems to predict customer growth and retention signals. Partner across engineering, sales, and product teams using scalable MLOps practices. Directly influence revenue strategy through automated, data-driven scoring and insights.

Responsibilities

• Build and deploy end-to-end machine learning models from exploration through production that score customer expansion likelihood and churn risk, directly informing revenue strategy.
• Design and maintain automated pipelines for model retraining, monitoring, and incident response, ensuring prediction accuracy and system reliability at scale.
• Partner with engineering and product teams to define telemetry schemas and data contracts, ensuring high-quality inputs that support longitudinal user behavior modeling.
• Conduct exploratory analyses and experiments to diagnose conversion changes, validate product hypotheses, and deliver actionable recommendations to senior leadership.
• Communicate findings and model outcomes to cross-functional stakeholders, translating complex results into clear narratives that guide sales, product, and customer success decisions.

Requirements

• 7+ years in product analytics or applied data science.
• Demonstrate deep proficiency in Python (including ML libraries) and SQL for data modeling, analysis, and production model development.
• Show experience building, deploying, and monitoring machine learning models in production environments with real business impact.
• Apply solid foundations in statistics, experimentation design, and causal inference to ambiguous business problems.
• Exhibit experience working with product telemetry or event-stream data to model user behavior and lifecycle transitions.
• Communicate complex technical findings clearly to non-technical stakeholders, including senior leadership.

Skills

• Python
• SQL
• ML libraries
• MLOps platforms (MLflow, SageMaker, or Vertex AI)
• Data transformation tools (dbt or Snowflake)
• Model serving frameworks
• Data quality tooling
• Observability platforms

Work Type

• Hybrid
• Remote
• In-Person

Experience Level

• 7+ years

Salary/Compensations

• Minimum: $124,000.00
• Maximum: $271,200.00

Benefits

• Variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health; support work-life balance; and contribute to their community in meaningful ways.

About the Company

• Zoomies help people stay connected so they can get more done together.
• We set out to build the best collaboration platform for the enterprise, and today help people communicate better with products like Zoom Contact Center, Zoom Phone, Zoom Events, Zoom Apps, Zoom Rooms, and Zoom Webinars.
• We’re problem-solvers, working at a fast pace to design solutions with our customers and users in mind.
• Find room to grow with opportunities to stretch your skills and advance your career in a collaborative, growth-focused environment.

Equal Opportunity

• At Zoom, we believe great work happens when people feel supported and empowered.
• We’re committed to fair hiring practices that ensure every candidate is evaluated based on skills, experience, and potential.
• If you require an accommodation during the hiring process, let us know—we’re here to support you at every step.
• If you need assistance navigating the interview process due to a medical disability, please submit an Accommodations Request Form and someone from our team will reach out soon.
• This form is solely for applicants who require an accommodation due to a qualifying medical disability.
• Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed.
• Our interviews are supported by BrightHire, a tool that helps us create a consistent and thoughtful interview experience and may include recordings.
• Please refer to our candidate privacy statement for more information of how we use your data.