Analytics & Data Science Leader

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.

• →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

• 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

• 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