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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · AI tool adoption waves
The team progressively adopted AI productivity tools — Ava in late 2023, Cursor agents in early 2025, and Claude in 2026 — each wave producing a substantially larger acceleration in output.
Tools used
AvaCursorClaudeLLMGitHubMCPsPython
Outcome
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.
What failed first
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.