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# Data Scientist

- **Company:** Zoom Video Communications, Inc.
- **Location:** San Jose, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $87,600.0 - $186,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Airflow, Amazon Web Services, Microsoft Azure, Big Data, Data Infrastructure, Data Visualization, Database Queries, Python (Programming Language), Machine Learning, NumPy, Recommender Systems, Power BI, Tableau (Software), Pandas, Matplotlib, Scikit Learn, Information Technology, Plotly, Machine Learning Operations, Data Pipelines, Databricks
- **Published:** October 22, 2026
- **Apply:** https://zoom.wd5.myworkdayjobs.com/Zoom/job/San-Jose-CA/Data-Scientist_R19777-1

## About the Role

* Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field, or equivalent experience.
 * 2+ years of hands-on experience in a data science or similar role, with a focus on building consumer-facing machine learning models.
 * Proficiency in Python (pandas, scikit-learn, numpy) and strong SQL skills for querying and manipulating large-scale datasets.
 * Experience with A/B testing, customer segmentation, LTV prediction, or personalization models in a product or e-commerce context.
 * Familiarity with data visualization tools such as Tableau, Power BI, or Python libraries (matplotlib, seaborn, plotly).
 * Strong problem-solving and communication skills, with comfort navigating ambiguity and translating analytical findings for diverse audiences.
 * Exposure to cloud platforms (AWS, GCP, or Azure) or tools such as dbt, Databricks, Airflow, or MLflow is a plus.

## Description

* Analyze large, complex datasets to identify trends and behavioral patterns related to buying journeys, customer intent, and user engagement across Zoom's self-serve products.
 * Build and validate predictive models and machine learning algorithms supporting personalization, churn prediction, customer segmentation, and recommendation systems.
 * Design and execute A/B tests, interpret results with statistical discipline, and translate findings into clear business recommendations.
 * Collaborate with stakeholders across product, engineering, and marketing to understand business objectives and develop targeted analytical solutions.
 * Communicate insights effectively to both technical and non-technical audiences through well-structured presentations and visualizations.
 * Partner with Data Engineers on pipeline development to ensure robust, scalable data infrastructure that supports model production.
 * Monitor model performance continuously and iterate on solutions to maintain accuracy and relevance as business conditions evolve.