AI Analyst

Emergent builds autonomous coding agents that replace traditional software development by generating, testing, and deploying production applications directly from plain-language intent. Our systems run in production at global scale and are used to build millions of real applications.

Since our public launch, we've crossed $130M in Annualised Revenue and grown to over 10M users across 190+ countries, who have built 12M+ applications on Emergent. We're backed by Creaegis, Khosla Ventures, SoftBank, Lightspeed, Together, Y Combinator, Google, Claypond and Sentinel Global.

We're solving the hard part of AI-driven software creation: correctness, reliability, security, and scale in real production systems. The team is built by repeat founders, Olympiad medalists, IIT & IIM alumni, and leaders from Google, Amazon, and Dropbox.

We're hiring builders who want ownership, speed, and impact at global scale.

The Role:
We're looking for an AI Analyst who can own the full analytics lifecycle at a fast-scaling vibe coding platform, from raw data exploration to production dashboards to strategic recommendations. This isn't a traditional analyst role. You'll use AI tools (Claude, MCP integrations, agentic workflows) as force multipliers to automate repetitive work, accelerate hypothesis testing, and ship insights at startup speed. You'll sit at the intersection of product, growth, and finance, investigating conversion funnels one hour, building cohort retention models the next, and stress-testing a pricing change by end of day.

What You'll Do:

• Own subscription and product analytics end-to-end: conversion funnels, cohort retention, churn analysis, LTV modeling, and revenue tracking
• Build and maintain production dashboards and alerting systems that the team relies on daily for business-critical metrics
• Conduct deep exploratory analysis on user behavior, identifying engagement patterns, diagnosing drop-offs, and quantifying the impact of product and pricing changes
• Write highly optimized SQL against a modern data stack (BigQuery, PostgreSQL), including complex CTEs, window functions, and cross-database joins
• Maintain and evolve the company's analytics knowledge base, documenting schemas, metric definitions, and known data quality issues so the team can self-serve
• Partner with product and engineering to define experiments, validate A/B tests, and ensure proper attribution across channels
• Use AI tooling (Claude, MCP servers, agentic pipelines) to automate data exploration, query generation, anomaly detection, and reporting, treating AI as a core part of your workflow, not an afterthought