The opportunity
Instacart’s Marketing Data Science and Analytics team partners across Marketing, Strategic Finance, and Product to power data-driven growth. As a Senior Marketing Decision Scientist II, you will shape how we measure, forecast, and optimize marketing performance across channels,…
What you'll do
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.
What they're looking for
- + 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.
