Analytics & Data Science Leader

About this role

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 Analytics Leader to own the entire data and analytics function at one of the fastest-scaling AI platforms in the world. You'll define what we measure, how we measure it, and how data drives every major decision across product, growth, finance, and leadership.

This is a player-coach role. You'll set the analytics vision, build and lead a high-performing team, and still stay close enough to the data to pressure-test a pricing model or debug an attribution issue yourself. You'll report directly to leadership and act as the single source of truth for business-critical metrics: revenue, retention, conversion, and unit economics. Critically, this is an AI-native analytics leadership role. You'll build a function where AI tools (Claude, MCP integrations, agentic pipelines) are core infrastructure, not add-ons, enabling a lean team to deliver the output of one many times its size.

What You'll Do:

• Own the company-wide data science and analytics strategy: define the metrics framework, predictive models, north-star KPIs, and reporting cadence used by leadership, product, growth, and finance

• Build, hire, and lead the data science and analytics team, setting the bar for rigor, speed, and self-serve enablement across the company

• Own subscription and revenue analytics end-to-end: MRR, churn, cohort retention, LTV/CAC, conversion funnels, and usage-based billing models

• Lead applied data science initiatives: churn and LTV prediction, propensity and conversion models, forecasting, anomaly detection, and segmentation to drive product and growth decisions

• Architect and govern the modern data stack (BigQuery, PostgreSQL, event pipelines), partnering with engineering on data quality, schema design, and pipeline reliability

• Establish experimentation as a discipline: design the A/B testing framework, define statistical standards and causal inference methods, and ensure proper attribution across channels

• Deliver strategic analysis and modeling for high-stakes decisions: pricing changes, market expansion, product bets, and fundraising narratives

• Build production dashboards, ML-powered alerting systems, and forecasting tools that leadership relies on daily, and evolve the knowledge base so teams can self-serve

• Champion AI-native data science: deploy Claude, MCP servers, and agentic workflows to automate exploration, feature engineering, anomaly detection, query generation, and reporting at scale

• Act as the trusted data and modeling partner to the CEO and functional leaders, translating complex analysis and models into clear, decision-ready recommendations

Who You Are:

• 12+ years in data science, analytics, or a related quantitative field, with 5+ years leading and scaling data science and analytics teams at high-growth B2C/SaaS or PLG companies

• Deep expertise in subscription and SaaS metrics: MRR, churn, cohort analysis, LTV modeling, conversion funnels, and usage-based billing

• Strong foundation in statistical modeling and applied machine learning: regression, classification, time-series forecasting, and propensity/uplift modeling, with the judgment to know when a simple model beats a complex one

• Elite SQL proficiency: you think in CTEs and window functions, understand partitioning tradeoffs, and validate results against multiple sources instinctively

• Proven track record of building data science and analytics functions from scratch or through hypergrowth: hiring, tooling, metric and model governance, and stakeholder trust

• Strong command of the modern data stack: BigQuery or similar warehouses, dbt, product analytics tools (PostHog, Mixpanel, Amplitude), and BI platforms

• Experimentation depth: you've designed and governed A/B testing programs and understand statistical rigor, causal inference, identity stitching, and multi-touch attribution

• A hypothesis-driven operator: you form a thesis, test it iteratively, build models to validate it, and revise when the data disagrees, and you've taught teams to do the same

• Genuine conviction in AI-native workflows: you use AI assistants and agentic tools daily and have strong opinions on how they transform data science and analytics work

• Executive-grade communication: you can walk into a board meeting or a leadership review and land a data-backed recommendation in five minutes

• Comfort with ambiguity and messy, evolving data infrastructure: you unblock yourself and your team without waiting for perfect pipelines

Nice to Have:

• Experience at a developer tools, AI, or vibe coding platform

• Strong Python fluency for statistical modeling, ML, and automation (pandas, scikit-learn, statsmodels, or similar)

• Prior ownership of finance-adjacent analytics: revenue recognition, forecasting, and unit economics for board reporting

• Experience partnering directly with engineering on event-driven data models and behavioral analytics

• Experience deploying models into production (not just notebooks), with MLOps fundamentals a plus

• Early-stage startup experience where you built the data…