Analytics & Data Science Leader at Emergent — Bengaluru, IND | Base Career

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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

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 science and analytics layer from zero to scale

Analytics & Data Science Leader

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