Senior Marketing Decision Scientist II

About the Job

• Own the end-to-end marketing measurement strategy across paid search, paid social, display, affiliates, CTV, and lifecycle/CRM, unifying MMM, MTA, and incrementality testing to guide channel and portfolio-level investment.
• Design, launch, and analyze experiments (e.g., geo tests, PSA tests, holdouts) and causal inference studies that quantify lift, inform targeting, and establish best practices for decision-making under uncertainty.
• Build and productionize predictive models (e.g., LTV, churn/propensity, audience response, budget allocation) using SQL and Python or R, partnering with data engineering to automate pipelines and ensure data quality.
• Create executive-ready dashboards and narratives in tools like Looker or Mode that track KPIs, explain performance drivers, and translate insights into clear, prioritized recommendations.
• Partner with Strategic Finance and Marketing leadership on forecasting, scenario planning, and quarterly planning processes; influence roadmaps and present findings to VP+ stakeholders.
• Prioritize ruthlessly in a dynamic environment, managing multiple concurrent projects and elevating the team's analytical bar through peer reviews, documentation, and mentorship.

About You

Minimum Qualifications

• 6+ years of experience in marketing analytics or data science within technology, e-commerce, marketplace, or consumer subscription businesses.
• Advanced proficiency in SQL and in either Python or R for data manipulation, statistical analysis, and modeling.
• Hands-on experience designing and analyzing marketing experiments (e.g., A/B tests, geo experiments, holdouts) and applying causal inference techniques to estimate incrementality.
• Proven track record implementing at least one marketing measurement approach (e.g., MMM, MTA, or structured incrementality testing) to inform budget allocation for multi-million-dollar programs.
• Experience building business-facing dashboards and self-serve tools in Looker, Tableau, or Mode.
• Experience working with modern data warehouses (e.g., Snowflake, BigQuery, or Redshift) and version control (Git).
• Demonstrated ability to translate ambiguous business questions into analytical roadmaps and to communicate clear, actionable recommendations to non-technical and executive audiences.
• Bachelor's degree in a quantitative field (e.g., Statistics, Economics, Computer Science, Mathematics, Engineering) or equivalent practical experience.

Preferred Qualifications

• 8+ years of relevant experience; advanced degree (MS/PhD) in a quantitative discipline.
• Experience building, validating, and operationalizing Marketing Mix Models (preferably Bayesian approaches using PyMC, Stan, or similar) and triangulating MMM with experiment results.
• Familiarity with privacy-conscious measurement (e.g., conversion modeling, SKAN, clean rooms such as Amazon Marketing Cloud or Ads Data Hub) and ad platform APIs.
• Experience with analytics engineering and pipeline tooling (e.g., dbt, Airflow) and strong data QA practices.
• Background in lifecycle/CRM analytics (e.g., uplift modeling, audience selection, message experimentation) and LTV forecasting.
• Exposure to experimentation platforms and feature flagging (e.g., Optimizely or internal frameworks) and to ML applications for bidding, pacing, and creative optimization.
• Experience mentoring peers and elevating analytical standards through code reviews, reproducible research, and documentation.