Nextdoor

Head of Data Science – Monetization

Nextdoor

full-time

Posted on:

Location Type: Remote

Location: United States

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Salary

💰 $199,000 - $300,000 per year

Job Level

Lead

About the role

  • Developing, communicating, and leading execution of the data science strategy across Nextdoor’s monetization products
  • Owning the monetization DS roadmap: identify the highest-impact opportunities and risks, define clear success metrics, and align stakeholders on priorities
  • Raising the bar on DS craft excellence: establish standards for strong analysis, experimentation, and impact-orientedness; provide high-quality review and mentorship; and help the team deliver work that is rigorous, decision-relevant, and well-communicated
  • Being a hands-on technical leader: contribute on the framing and execution of the most strategic, ambiguous, and high-leverage problems (e.g., measurement strategy for new products, experiment design standards, causal questions, marketplace dynamics)
  • Driving experimentation and measurement: design and evaluate experiments, ensure sound interpretation, and strengthen the end-to-end measurement system (instrumentation, definitions, dashboards, and decision forums)
  • Partnering cross-functionally to move strategy forward: serve as a thought partner to Product, Engineering, Sales, and other functions—bringing clarity, structure, and constructive data-informed judgment, especially when tradeoffs are complex
  • Building scalable systems and leverage: improve team throughput through reusable frameworks (metric definitions, analysis templates, audience definitions, and tooling) and by reducing repeated, one-off work
  • Leading, growing, and developing the team: hire and develop data scientists, create an inclusive and high-performing culture, and ensure strong career growth and performance coaching
  • Representing Data Science and a data-informed POV in leadership discussions: communicate clearly to varied audiences—from deep technical reviews to executive narratives—while maintaining rigor and intellectual honesty

Requirements

  • 10+ years in data science, product analytics, applied statistics, machine learning, or equivalent experience; advanced degree in a quantitative field strongly preferred
  • Hands-on experience in advertising or monetization data science for consumer products (e.g., ads marketplaces, targeting/relevance, measurement, auction dynamics, yield optimization, budgeting/pacing, attribution, or related domains)
  • Proven people leadership experience, including hiring and managing a team of data scientists across multiple problem areas, with a track record of growing others and raising team standards
  • Strong DS craft fundamentals: expertise in experimentation, measurement, and statistical reasoning; ability to choose the right method for the question and to anticipate pitfalls and bias
  • Experience leading complex analyses end-to-end (problem framing → data requirements → analysis/modeling → recommendations → iteration)
  • Comfort partnering across the product development spectrum (strategy, ideation, instrumentation, experimentation, and—where appropriate—model development)
  • Exceptional communication: ability to explain complex ideas clearly, influence decisions, and write narratives that drive alignment and action
  • Appetite for applying AI and emerging tools to increase productivity and reimagine how DS teams deliver impact (without sacrificing rigor and decision quality)
  • Eagerness to explore and apply AI and emerging technologies to reimagine how work gets done.
Benefits
  • Health plans with a 100% covered employee only plan option
  • OneMedical membership for concierge care
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
data scienceproduct analyticsapplied statisticsmachine learningexperimentationmeasurementstatistical reasoningcomplex analysesAIemerging technologies
Soft Skills
people leadershipcommunicationinfluencementorshipteam developmentproblem framingclaritystructuredecision qualitycollaboration