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ABOUT HAPPYROBOT HappyRobot is the infrastructure for enterprises to build and orchestrate AI workforces. Our AI workers don't just communicate through voice and email - they make decisions, take action, and run operations autonomously across entire enterprise systems. Born in Y Combinator (S23) and backed by a16z, Base10, Prysm Capital and Eurazeo with over $150M raised, we power critical operations for global enterprises worldwide. Our platform is battle-tested in the most demanding environments, where AI has real consequences. We started in logistics, built our 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. Learn more about our vision in our manifesto. https://www.happyrobot.ai/blog/manifesto ABOUT THE ROLE You’ll help make data a core part of how we build and improve HappyRobot’s products. You’ll work closely with Product, Engineering, and Machine Learning teams to measure how changes to our models, agents, and product features affect real-world performance. You’ll define meaningful metrics, design experiments, and conduct deeper analyses to understand how our agents create value for clients. Your work will range from evaluating A/B tests and model changes to analyzing millions of conversations and workflows. You’ll turn complex data into clear insights that influence our product and ML roadmaps. 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 the performance of our agents affects client outcomes, such as task completion, operational efficiency, response quality, and 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 differences in performance. - 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. MUST HAVE - 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 the ability to distinguish statistical significance from meaningful product or customer 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, and 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 an

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