How AI changes the role of applied scientists at Instacart's Economics Team
The Economics Team's portfolio was concentrated in standardized Core ML tasks that occupied 73% of PRs, while entire task categories — especially frontend development — were inaccessible due to skill gaps and the coordination costs of delegating to specialist engineering teams.
The team's Causal Inference Platform (CIP), a UI-based tool giving company-wide access to standard causal inference methods, was undermined by AI coding assistants that made bespoke causal inference faster and more flexible than any constrained UI could offer.
AI tools drove a near-doubling of PRs and lines of code relative to the 2023H1 baseline, a 33% increase in task category diversity per team member, and enabled economists to build full-stack web applications like Apex end-to-end — work that previously required coordination with separate engineering teams.
Show all 9 reported metrics
Frequently asked questions
What did this team achieve with this AI workflow?
AI tools drove a near-doubling of PRs and lines of code relative to the 2023H1 baseline, a 33% increase in task category diversity per team member, and enabled economists to build full-stack web applications like Apex…
What tools did this team use?
Ava, Cursor, Claude, LLM, GitHub, MCPs, Python.
What results were reported?
PRs and lines of code: essentially doubling; Unique task categories per team member: 33%; New task categories added per member: approximately 1.3; Work outside primary task category: ~37% (source-reported, not independently verified).
What failed first in this deployment?
The team's Causal Inference Platform (CIP), a UI-based tool giving company-wide access to standard causal inference methods, was undermined by AI coding assistants that made bespoke causal inference faster and more fl…
How is this back office ops AI workflow structured?
AI tool adoption waves → LLM PR classification → Task redistribution → Apex full-stack delivery → Agentic causal inference.