Data Scientist

HappyRobot is the infrastructure for enterprises to build and orchestrate AI workforces. Their AI workers don't just communicate — they make decisions, take action, and run operations autonomously across voice, email, and enterprise systems. Born in Y Combinator (S23) and backed by a16z and Base10 with over $60M raised, HappyRobot powers critical operations for global enterprises worldwide. Their platform is battle-tested in the most demanding environments — where AI has real consequences. They started in logistics, built their own voice stack, models, and orchestration layer from the ground up, and are now bringing that infrastructure to every enterprise that runs the real economy. About the Role: You'll help make data a core part of how HappyRobot builds and improves its products. You'll work closely with Product, Engineering, and Machine Learning teams to measure how changes to models, agents, and product features affect real-world performance. You'll define meaningful metrics, design experiments, and conduct deeper analyses to understand how the company's agents create value for clients. What You'll Do: Define and track product, feature, and agent-level metrics Design, run, and interpret A/B tests for model changes, prompts, agent behavior, workflows, and product features Measure how agent performance affects client outcomes (task completion, operational efficiency, response quality, automation rates) Connect offline model evaluations with production performance and real-world customer impact Conduct deep analyses across conversations, workflows, and product usage to identify opportunities and explain performance differences Investigate anomalies and regressions, perform root-cause analyses, and recommend improvements Build statistical models, simulations, and analytical frameworks to support product and ML decisions Partner with Engineering to improve instrumentation, data quality, experimentation systems, and analytical data models Build dashboards and self-serve tools that help teams understand product and agent performance Communicate findings and recommendations clearly to technical and non-technical stakeholders Why Join Us: Opportunity to work at a high-growth AI startup backed by a16z, Y Combinator, and Base10 Ownership & Autonomy — take full ownership of projects and ship fast Top-Tier Compensation — competitive salary + equity Comprehensive Benefits — healthcare, dental, vision coverage Work with a world-class team of engineers and builders Operating Principles: Extreme Ownership, Craftsmanship, "Majos" (be a good human), Urgency with Focus, Talent Density and Meritocracy, First-Principles Thinking

4+ years of experience in Data Science, Product Analytics, or another highly quantitative product role Strong experience with experimental design, A/B testing, statistics, causal inference, and hypothesis-driven analysis Advanced proficiency in SQL and Python Experience defining and operationalizing product and feature metrics Ability to translate ambiguous product questions into rigorous analyses and actionable recommendations Strong product instincts and ability to distinguish statistical significance from meaningful impact Experience partnering closely with Product, Engineering, or Machine Learning teams Strong written and verbal communication skills High attention to detail and commitment to analytical accuracy Founder mindset: ownership, independence, curiosity, willingness to go deep Nice to Have: Experience working with large language models, AI agents, generative AI, or other probabilistic ML products Experience measuring the production impact of model, prompt, retrieval, or orchestration changes Familiarity with ML evaluation systems and the relationship between offline evaluations and online metrics Experience analyzing conversational, NLP, speech, or other unstructured data Experience with enterprise or B2B products Experience combining quantitative analysis with qualitative methods (conversation reviews, customer feedback, surveys, user research) Familiarity with modern analytics infrastructure, data warehouses, experimentation platforms, and BI tools Prior experience in a fast-growing startup or highly ambiguous environment