Staff Data Scientist, Product, Core Data

Google is seeking a Staff Data Scientist to join their Core team, which is responsible for building the technical foundation behind Google's flagship products. This role combines advanced analytics, technical leadership, and strategic thinking to drive improvements across Google's product ecosystem.

The position requires a strong quantitative background with at least 10 years of experience in analytics and coding. You'll be working with tools like Python, R, and SQL to perform complex statistical analyses and develop solutions for ambiguous problems. The role involves both technical execution and leadership responsibilities, including overseeing projects and developing team capabilities.

As a Staff Data Scientist, you'll be part of the team that owns and advocates for underlying design elements, developer platforms, and infrastructure at Google. Your work will directly impact technical decisions across the company, helping to break down technical barriers and strengthen existing systems.

The compensation package is competitive, with a base salary range of $183,000-$271,000, plus bonus, equity, and comprehensive benefits. The position offers flexibility in location, with opportunities in major tech hubs including Sunnyvale, Kirkland, and New York.

This is an excellent opportunity for an experienced data scientist who wants to work on challenging problems at scale, lead important technical initiatives, and make a significant impact on Google's core products and infrastructure. The role combines technical depth with strategic thinking, requiring both strong analytical skills and the ability to lead and influence across organizations.

Google offers a collaborative environment with opportunities to work with talented colleagues and access to cutting-edge technology and data. The company is committed to diversity and inclusion, providing equal opportunities and fostering a culture of belonging. This role is perfect for someone who wants to shape the future of Google's technical infrastructure while working with some of the most interesting and complex datasets in the technology industry.