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HappyRobot is seeking an analyst to turn fuzzy product questions into clear measurements and tests for AI agents that handle calls and emails. You will design experiments, apply statistics, and use SQL and Python to quantify performance of voice and email agents in real operations.
You will link lab scores to customer outcomes, analyze regressions on large conversation datasets, choose metrics that reflect real value, and communicate findings to engineers and non-technical stakeholders.
The role asks for someone who can turn fuzzy product questions into clear measurements and tests for AI agents that handle calls and emails. A strong candidate brings solid statistics, experiment design and causal thinking, plus strong SQL and Python. They also have the judgment to tell a statistically real result from one that matters to a customer, and can explain findings to both engineers and non-technical people.
AI agents give different answers each time, so measuring them takes more care than testing a normal software feature. The analyst links lab model scores to what happens in real customer operations, and digs into regressions across huge volumes of conversations. Deciding which metrics reflect real value, and persuading a team to act on them, stays a human job.
Working here would mean measuring how changes to models, prompts and workflows affect voice and email agents running real operations for enterprise clients. To build this experience beforehand, someone could practice evaluating an LLM app, comparing offline test scores with live results, and reviewing transcripts by hand alongside the numbers.
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AI voice and email agents that autonomously handle the phone calls and operational busywork of supply chain and enterprise teams.