Sr. Decision Scientist, Platform Excellence Ops Analytics

Overview

As a Senior Decision Scientist, you will be responsible for understanding and enhancing the quality of Instacart's online grocery marketplace and fulfillment operations. You will transform data-driven insights into impactful actions that drive measurable business outcomes. In this role, you will conduct advanced technical analyses/modeling that help optimize marketplace and retailer performance. Working at the intersection of operations and analytics, you will partner with cross-functional teams across R&D to deliver solutions that improve both operational efficiency and consumer satisfaction. 

The Platform Excellence Operations (PEO) team is dedicated to improving the quality of Instacart’s online grocery experience for our customers, shoppers, and retailers. The analytics team focuses on identifying, understanding, and eliminating defects in the online grocery experience by analyzing and interpreting operational data. You’ll be part of a dynamic cross-functional team that collaborates closely with stakeholders to provide critical data insights to enhance our operations and overall customer experience.

As part of the Marketplace & Retailer Pillar within PEO Analytics, you will tackle critical fulfillment quality challenges for our key strategic partners. You will play an essential role in maintaining Instacart's reputation for reliability and operational excellence by analyzing and optimizing marketplace & retailer level fulfillment performance

About the Job

• Analysis and Strategy 
  • Conduct rigorous analyses grounded in a deep understanding of marketplace dynamics, controlling for confounding factors to ensure insights are accurate, trustworthy, and actionable
  • Leverage advanced statistical techniques including causal inference and regression modeling to isolate true drivers of fulfillment quality and uncover insights that would otherwise remain hidden in observational data
  • Balance short-term operational needs with long-term strategic planning for quality improvement and sustainable growth
  • Provide thought leadership within the pillar when planning future strategic initiatives and roadmaps to maximize the impact of data-driven insights
• Data Engineering and Operational Efficiency
  • Build data pipelines that monitor and analyze key marketplace and retailer quality issues
  • Develop automated workflows that streamline issue detection, root cause analysis, and recommendations for operational teams and stakeholders
  • Build and maintain data models and pipelines

About You

Minimum Qualifications

• 5+ years of experience in analytics, data science, or decision science roles
• Familiarity with: Snowflake/Databricks/BigQuery or similar data warehouses, DBT/Apache Airflow or similar orchestration tools, Github, and Jira 
• Proficiency with Python/SQL to perform statistical analysis, predictive modelling, and large language modelling and automation
• Ability to deal with ambiguity; a self-starter with the ability to quickly respond to problems independently
• Experience building and maintaining DBT models for analytics use cases, including managing downstream dependencies and understanding how analysts consume those datasets
• Strong problem solving skills - able to quickly debug anomalies in performance metrics, form hypothesis for investigation, and draw conclusions by conducting nuanced analysis
• Effective prioritization and ability to balance quality and speed in a time constrained environment
• Ability to work independently with a strong sense of ownership
• Strong verbal and written communication skills, including the ability to lead meetings and synthesize complex topics by creating compelling narratives for various stakeholders
• Experience working directly with product/engineering teams

Preferred Qualifications

• Experience designing and analyzing experiments (A/B tests, randomized controlled trials) to measure causal impact and inform business decisions
• Understanding of multi-sided marketplace dynamics and the unique analytical challenges of balancing supply, demand, and platform economics
• Familiarity with LLM performance metrics, quality assessment techniques, and best practices for prompt engineering or model evaluation
• Experience mentoring and coaching other data team members