Data Scientist

Zoom•San Jose, CA

3d•$87,600 - $186,000

About The Position

As a Data Scientist on Zoom's Online business team, you will apply machine learning and statistical modeling to understand user behavior, predict buying intent, and inform product decisions affecting Zoom users. You will build and validate predictive models, run experiments, and translate analytical findings into recommendations for cross-functional partners. The role combines exploratory analysis with production-ready modeling and contributes to growth decisions that shape user outcomes at scale. The Data Science team supports Zoom's growth strategy across product, marketing, and engineering through data-driven insights and machine learning. Its work includes customer segmentation, churn modeling, personalization, and growth experimentation, with a focus on improving user experience and business outcomes.

Requirements

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field, or equivalent experience.
At least 2 years of hands-on experience in data science or a similar role, focused on building consumer-facing machine learning models.
Proficiency in Python, including pandas, scikit-learn, and 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 including matplotlib, seaborn, and plotly.
Strong problem-solving and communication skills, comfort navigating ambiguity, and ability to translate analytical findings for diverse audiences.

Nice To Haves

Exposure to cloud platforms such as AWS, GCP, or Azure, or tools such as dbt, Databricks, Airflow, or MLflow.

Responsibilities

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