Analytics & Data Science Leader

<p>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.</p> <p>Since our public launch, we've crossed <strong>$130M in Annualised Revenue</strong> and grown to <strong>over 10M users across 190+ countries</strong>, who have built <strong>12M+ applications</strong> on Emergent. We're backed by <strong>Creaegis, Khosla Ventures, SoftBank, Lightspeed, Together, Y Combinator, Google, Claypond and Sentinel Global.</strong></p> <p>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 <strong>repeat founders, Olympiad medalists, IIT &amp; IIM alumni,</strong> and leaders from <strong>Google, Amazon, and Dropbox.</strong></p> <p>We're hiring builders who want ownership, speed, and impact at global scale.</p> <p><strong>The Role:</strong><br>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.</p> <p>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.</p> <p><strong>What You'll Do:</strong></p> <ul> <li>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</li> <li>Build, hire, and lead the data science and analytics team, setting the bar for rigor, speed, and self-serve enablement across the company</li> <li>Own subscription and revenue analytics end-to-end: MRR, churn, cohort retention, LTV/CAC, conversion funnels, and usage-based billing models</li> <li>Lead applied data science initiatives: churn and LTV prediction, propensity and conversion models, forecasting, anomaly detection, and segmentation to drive product and growth decisions</li> <li>Architect and govern the modern data stack (BigQuery, PostgreSQL, event pipelines), partnering with engineering on data quality, schema design, and pipeline reliability</li> <li>Establish experimentation as a discipline: design the A/B testing framework, define statistical standards and causal inference methods, and ensure proper attribution across channels</li> <li>Deliver strategic analysis and modeling for high-stakes decisions: pricing changes, market expansion, product bets, and fundraising narratives</li> <li>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</li> <li>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</li> <li>Act as the trusted data and modeling partner to the CEO and functional leaders, translating complex analysis and models into clear, decision-ready recommendations</li> </ul> <p><strong>Who You Are:</strong></p> <ul> <li>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</li> <li>Deep expertise in subscription and SaaS metrics: MRR, churn, cohort analysis, LTV modeling, conversion funnels, and usage-based billing</li> <li>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</li> <li>Elite SQL proficiency: you think in CTEs and window functions, understand partitioning tradeoffs, and validate results against multiple sources instinctively</li> <li>Proven track record of building data science and analytics functions from scratch or through hypergrowth: hiring, tooling, metric and model governance, and stakeholder trust</li> <li>Strong command of the modern data stack: BigQuery or similar warehouses, dbt, product analytics tools (PostHog, Mixpanel, Amplitude), and BI platforms</li> <li>Experimentation depth: you've designed and governed A/B testing programs and understand statistical rigor, causal inference, identity stitching, and multi-touch attribution</li> <li>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</li> <li>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</li> <li>Executive-grade communication: you can walk into a board meeting or a leadership review and land a data-backed recommendation in five minutes</li> <li>Comfort with ambiguity and messy, evolving data infrastructure: you unblock yourself and your team without waiting for perfect pipelines</li> </ul> <p><strong>Nice to Have:</strong></p> <ul> <li>Experience at a developer tools, AI, or vibe coding platform</li> <li>Strong Python fluency for statistical modeling, ML, and automation (pandas, scikit-learn, statsmodels, or similar)</li> <li>Prior ownership of finance-adjacent analytics: revenue recognition, forecasting, and unit economics for board reporting</li> <li>Experience partnering directly with engineering on event-driven data models and behavioral analytics</li> <li>Experience deploying models into production (not just notebooks), with MLOps fundamentals a plus</li> <li>Early-stage startup experience where you built the data science and analytics layer from zero to scale</li> </ul> <p><strong>Benefits and Perks:</strong></p> <ol> <li>Daily Meals: Lunch and Dinner provided</li> <li>Family Insurance: 5 Lakhs worth of coverage for you and your family</li> <li>Unlimited Paid Time Off: Take the time you need to recharge and come back refreshed</li> <li>Flexible Working Hours: Work arrangements that fit your life and commitments</li> </ol> <p>Let's build the future of software together.</p>