Senior Marketing Decision Scientist II
Lead marketing measurement and experimentation for Instacart. Own MMM, MTA, incrementality testing, predictive modeling, and dashboards to guide multi-million dollar marketing investments.
About the job
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.
Skills
SQL, Python, R, Looker, Tableau, Mode, Snowflake, BigQuery, Redshift, Git, Marketing Mix Modeling, Mmm, Mta, Causal Inference, A/B Testing
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