About this role
#TeamNextdoor
Nextdoor (NYSE: NXDR) is the essential neighborhood network. Neighbors, public agencies, and businesses use Nextdoor to connect around local information that matters in more than 350,000 neighborhoods across 11 countries. Nextdoor builds innovative technology to foster local community, share important news, and create neighborhood connections at scale. Download the app and join the neighborhood at nextdoor.com .
Meet Your Future Neighbors
As a Data Scientist at Nextdoor, you will help design and oversee product experiments and own complex analyses to drive company and product strategy. We use a semi-embedded team structure, in which a group of data team members works on a specific product or pillar, interfacing directly with product and engineering stakeholders. The Data Science group consists of people from a diverse set of backgrounds and perspectives, trained in fields as wide-ranging as economics, physics, statistics, and operations research. We are an integral part of the product development organization and play an active and collaborative role in building and improving the product.
At Nextdoor, we operate in an AI-first environment and expect every team member to actively use AI tools as part of their workflow. We aren't looking for prompt engineers; we’re looking for people who use tools like Claude, Gemini, ChatGPT, and Glean to challenge their own thinking and take full ownership of AI-assisted outputs.
We also offer a warm and inclusive work environment that embraces a hybrid employment model, blending an in office presence and work from home experience for our valued employees. The hiring team will go over these expectations with you if you are being considered for a role near one of our offices in San Francisco, Los Angeles, Chicago, Dallas, New York, and London.
The Impact You'll Make
We are looking for a Staff Data Scientist to accelerate execution on priority content investments and elevate the quality of product decision-making. This person will provide end-to-end data science ownership for new and emerging surfaces such as Video, Groups, and Events—areas with limited historical data that require strong product sense, pattern recognition, rapid hypothesis generation, rigorous diagnosis, and fast iteration. They will evaluate feature performance, including through non-standard A/B tests; identify opportunities to grow content supply, contributors, and engagement; partner with Distribution to improve discovery; and connect insights across surfaces to drive portfolio-level impact.
The role will also own the data architecture and quality requirements for content data assets spanning supply, creators, viewers, entities, content types, and surfaces. This includes evolving legacy structures, identifying coverage and quality gaps, and partnering with Analytics Engineering to build and maintain source-of-truth tables and artifacts—an increasingly important foundation for reliable agentic analytics. Finally,