AI Analyst - Product Intelligence & Business Analytics

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 public launch, Emergent has reached $100M ARR in 8 months. 6M+ users across 190+ countries have built 6.5M+ applications on Emergent. We've raised $100M+, backed by Khosla Ventures, SoftBank, Google, Lightspeed, Prosus, Together, and Y Combinator.

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.

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.

• →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

• 5+ years of strong SQL proficiency - you think in CTEs and window functions, you understand partitioning tradeoffs, and you instinctively validate results against multiple sources before drawing conclusions
• Experience with subscription/Saas metrics: MRR, churn, cohort analysis, conversion funnels, usage-based billing
• A hypothesis-driven mindset - you don't just pull numbers, you form a thesis, test it iteratively, and revise when the data disagrees
• Comfort with messy, evolving data infrastructure - you can navigate pipeline delays, schema inconsistencies, and incomplete data without getting stuck
• Familiarity with product analytics tools (PostHog, Mixpanel, Amplitude, or similar) and concepts like identity stitching and multi-touch attribution
• Experience building dashboards and visualizations that non-technical stakeholders actually use
• Eagerness to use AI assistants and agentic tools as part of your daily workflow - automating boilerplate, generating initial analyses, and iterating faster than a purely manual approach allows
• Strong async communication skills - you can document findings, flag issues, and propose next steps clearly enough that teammates can act without a follow-up meeting

• Experience with BigQuery, Redash, or similar BI tooling
• Familiarity with Python or dbt for data transformation
• Exposure to event-driven data models and behavioral analytics
• Comfort working directly with engineering teams on data pipeline issues
• Prior work at an early-stage startup where you built the analytics layer from scratch