Data Science Intern (2025 Start) – Mountain View, California, USA

Job Description

At Databricks, we are passionate about helping data teams solve the world’s toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world’s best data and AI infrastructure platform so our customers can use deep data insights to improve their business.

As a Data Science Intern, you will join the Data team of data scientists and engineers to turn Databricks business and operations data into insights for product design, strategies for customer acquisition/retention, and optimizations to engineering system efficiency/stability/performance. The Data team also functions as an internal “customer” that dogfoods the Databricks platform and drives product improvements.

We are hiring for the Mountain View Bay Area. We will discuss more with you as you move through the process!

The Impact You Will Have

• Work with the Data team and internal stakeholders (Product, Customer Success, Engineering, Sales, Marketing and Finance) to use data to solve problems
• Apply your expertise in data science methodologies such as causal inference modeling and recommender systems to real data to deliver insights and/or deploy algorithms to the Databricks platform
• Manage your own project end-to-end from data exploration to presenting insights to stakeholders  and/or deployment an algorithm in a product environment

What We Look For

• You will graduate in fall 2025 or spring 2026 with a Master’s or PhD degree in a quantitative field (e.g., Statistics, Math, Computer Science, Physics, Economics, Operational Research or Engineering)
• Proven ability to work with data in SQL and Python
• Experience with multiple statistical data analysis and machine learning methods such as generalized linear regression, regression and classification trees, unsupervised learning methods, causal inference, stochastic processes, time series forecasting
• You are excited to solve ambiguous problems with a collaborative team