Staff Data Scientist, Marketing

Role Overview

Staff Data Scientist, Marketing at Reddit’s Marketing Science team. You’ll lead the strategy and technical execution of Marketing Intelligence to measure and optimize Reddit’s marketing spend across both B2B and B2C growth.

What You’ll Do

• Set long-term marketing measurement strategy
  • Define a 2–3 year technical roadmap for marketing measurement.
  • Establish North Star metrics and measurement frameworks guiding hundreds of millions in marketing budget decisions.
• Optimize marketing ROI
  • Build and refine Media Mix Modeling (MMM) and Multi-Touch Attribution (MTA) to quantify incremental marketing impact.
• Bridge B2B and B2C growth
  • Create unified measurement frameworks for:
    • B2B: acquiring/retaining advertisers (revenue)
    • B2C: acquiring/engaging new Redditors (engagement)
  • Identify synergies between brand awareness and growth.
• Advance causal inference & experimentation
  • Lead always-on incrementality testing and geo-holdout experiments.
  • Measure long-term halo effects of brand marketing on organic growth.
• Lead through influence
  • Partner with Marketing, Growth, and Finance to turn insights into actionable budget shifts.
• Set technical bar and mentor
  • Mentor junior scientists and uphold code quality and statistical rigor across the Marketing org.

Requirements

• Education: Master’s or Ph.D. in Statistics, Economics, Mathematics, or related quantitative field.
• Experience:
  • 10+ years (if M.S.) in marketing science, growth data science, or econometrics; 6+ years (if Ph.D.).
• Domain expertise: Hands-on experience with MMM, incrementality testing, and Customer Lifetime Value (LTV) prediction.
• Technical skills: Advanced Python or R and SQL; experience with production-grade data pipelines and ML predictive modeling (e.g., churn prediction, lead scoring).
• Communication: Proven ability to influence C-suite stakeholders and translate statistical results into strategy.
• Collaboration: Comfortable working across Marketing, Growth, and Finance.

Preferred Qualifications

• Experience with Bayesian structural time series or causal inference libraries.