Nextdoor
Staff Data Scientist - Ads Marketplace
US Remote. Posted 2 months ago
The role
You will lead data science for Ads Marketplace/Delivery by shaping the roadmap and setting standards for experimentation. You will partner with ML engineers and lead deep-dive analyses to improve auction mechanics, bidding, and pacing.
What you will do
- Serve as the technical lead for ads delivery data science
- Set the standard for experimentation in a two-sided marketplace
- Partner with ML engineers on ranking, bid optimization, and pacing models
- Lead deep-dive analyses to surface opportunities and diagnose delivery issues
- Influence product strategy by translating complex findings into recommendations
- Mentor data scientists and raise the bar for analytical rigor
Required qualifications
- Ph.D. or M.S. in Economics, Operations Research, Statistics, Computer Science, Applied Mathematics, or related quantitative fields
- 10+ years of relevant Data Science experience in Ads Marketplace or Monetization experience
- Expert knowledge of SQL and Python programming, including numpy, pandas, and scikit-learn
- Excellent communication skills
- Deep expertise in statistical inference, advanced experimental design, and quantitative economic modeling
- Experience with ML-driven ads systems (CTR/CVR prediction, bid optimization, budget allocation)
- Eagerness to explore and apply AI and emerging technologies
Nice to have
- Familiarity with simulation, forecasting, or optimization techniques for marketplace design
- Experience with local, social, or performance advertising products
- Experience mentoring data scientists and building analytical culture
Benefits
- Variety of health plans
- 100% covered employee-only plan option
- OneMedical membership for concierge care
- Equity grant with quarterly vesting
About the company
Nextdoor is an essential neighborhood network used by neighbors, public agencies, and businesses to connect around local information. The company builds technology to foster local community and create neighborhood connections at scale.