Senior Data Scientist - Search

nextdoor; United States; Hybrid; USD 175,000 - 234,000; Full time

Benefits offered by this job

Equity grant

Hybrid work model

Job summary

nextdoor is seeking a senior data scientist focused on search quality for its large consumer platform that connects people, places, posts, and local businesses. You will blend deep analytics with hands-on model building, partnering with engineering, product, design, and ML teams to shape query understanding, retrieval, and ranking.

This hybrid role owns problems end-to-end from framing questions to shipping in production.

Qualifications

• 5+ years of relevant data science experience in search/retrieval or related product.
• Strong SQL and Python skills, with hands-on experience in ML modeling.
• Experience designing and analyzing product experiments and A/B tests.
• Ability to translate complex questions into data-driven insights.

Responsibilities

• Design and build search relevance, retrieval quality, and ranking algorithms.
• Plan and analyze experiments, monitor early feature performance and post-launch impact.
• Translate product questions into analyses and surface strategic insights.
• Develop dashboards and evaluation frameworks to track query understanding and engagement.
• Collaborate with product, design, engineering, marketing, and operations to embed data-driven decisions.
• Champion data quality across the company.

Skills

SQL

Python

NumPy

Pandas

Scikit-learn

Communication

Experimentation

Tools

Jupyter

Airflow

Job description

Role overview

A senior data scientist position focused on search quality, combining deep analytics with hands‑on model building. The role partners closely with engineering, product, design, and ML teams to shape query understanding, retrieval, and ranking across a large‑scale consumer platform that connects people, places, posts, and local businesses. It is a hybrid analyst‑plus‑builder opportunity, owning problems end‑to‑end from framing the question to shipping improvements in production.

Responsibilities

• Design and build algorithms that improve search relevance, retrieval quality, and ranking for both user‑facing discovery and business‑discovery use cases.
• Plan, run, and analyze A/B experiments, including early‑stage feature monitoring and post‑launch follow‑ups to confirm durable impact.
• Translate ambiguous product questions into data analyses that surface strategic insights, and evaluate the effect of ranking and content changes.
• Develop scalable metrics, dashboards, and evaluation frameworks that track performance across query understanding, retrieval, ranking, and downstream engagement.
• Partner with product, design, engineering, marketing, and operations to embed data‑informed decision making throughout development.
• Champion data quality and help colleagues across the company use data effectively.

Requirements

• 5+ years of relevant data science experience, ideally on a search, recommendation, or information retrieval product.
• Hands‑on familiarity with query understanding, retrieval, ranking, and the metrics used to evaluate them.
• Working knowledge of machine learning models, plus the judgment to choose the right approach and define meaningful evaluation metrics.
• Strong experience designing and analyzing complex product experiments, including interaction effects and long‑term metric movement.
• Expert SQL and Python skills, including scientific computing tools such as numpy, pandas, and scikit‑learn.
• Excellent communication skills, with the ability to simplify complex findings and tell a clear story to varied audiences including executives.

Nice to have

• Experience with ranking algorithms for unstructured content.
• Experience deploying or monitoring ML models in production.
• Familiarity with building retrieval‑augmented generation systems.

Benefits and work setup

• Hybrid work model combining in‑office and remote time, with offices in several major US cities and London.
• Salary range noted in the source material as approximately $175,000–$234,000 annualized, depending on skills, experience, proficiency level, and geographic location, plus a meaningful equity grant with quarterly vesting.