Posting record
Open 12+ days
- Simular's site dates the posting
- Ghostless first sees this listing
- Last checked: still up
How we know
It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.
Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Simular, so its true age is longer.
Last checked Nov 4, 2026.
How well do you match?
See how much of this posting your resume shows, and what it's missing.
Upload your resumeWhat it asks of you
- Must be a current student
- Bachelor's degree asked for
- Equivalent experience accepted instead of a degree
What the posting discloses
Not stated: Pay, real vacancy, AI screening.
Why these three
Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.
Some employers add pay only on their own site. Check the original posting before you rule it out.
Skills mentioned
About the role
Where multiple locations are listed for this role, the position may be based in any of those locations, with priority determined according to the order of listing.
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.
What you'll do
- 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 might be a fit if
- 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
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