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Agents generate an unusual amount of data: every run, every step, every recovery from failure. Buried in that data is the answer to which agent behaviors actually work and where users get stuck. Not enough of it is being read today.
You'll own analysis that shapes what we build next, working directly with product and engineering.
Build and maintain dashboards that track product health: activation, retention, task success, and agent reliability
Analyze agent run data to find where tasks fail, stall, or need human rescue
Answer product questions with data, and be honest when the data can't answer them
Help define and instrument the events and metrics we should be tracking in the first place
Support experiment design and read out results without overclaiming
Turn analysis into recommendations, with a clear view of what you'd do next
Make your work reproducible so others can build on it
You're pursuing a Bachelor's or Master's degree in Statistics, Computer Science, Economics, Engineering, or a related field, or equivalent experience
You're solid with SQL and comfortable in Python or R for analysis
You've built a dashboard or analysis someone else actually used
You understand the basics of experimentation and are appropriately skeptical of noisy results
You can explain a finding to someone who won't read the notebook
You're curious about the product, not just the numbers
Bonus: experience with product analytics tools, dbt, or working with event data at scale
