Senior Marketing Decision Scientist II

Instacart

2h•CA$168,000 - CA$177,500•Remote

About The Position

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, helping Instacart make smarter investment decisions and accelerate customer acquisition and retention. This is a high-impact, high-visibility role on a small, focused team where you will own complex, zero-to-one measurement initiatives and scale proven solutions. You will collaborate closely with channel marketers, growth leaders, finance partners, and data engineers to deliver models and experimentation frameworks that inform multi-million-dollar decisions. If you thrive in a fast-paced environment that still moves like a startup—and you love rolling up your sleeves to turn ambiguous questions into clear recommendations—this role is for you. You’ll join a tight-knit immediate team of 5 within a broader 9-person marketing data science org, where there is real scope to set the bar for analytical rigor, build systems that last, and influence the roadmap. Come help us go far together by solving complex problems that grow the pie for our customers, retailers, and partners.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.