Needs more than a quick edit9 of 12 checks passed
The output misses the VP Engineering big-tech pattern and does not define role competencies, so a PM would need to add those before using it.
Output
To: Elif Demir, VP Product From: Director of Product Subject: PM interview loop: what the data says and what to change next quarter
What seems to predict performance
We have records for 52 final-round candidates, including 8 hires with 12-month ratings. Those hires averaged 3.5, versus a company PM average of 3.4; four were below 3.4, including two rated 2.
Among the eight hires, the clearest signal is the behavioural metric round. Its score has a strong positive association with 12-month ratings (exploratory Pearson r ≈ +0.93). The four hires rated 4–5 averaged 3.75 on this round, versus 2.5 for the four rated 2–3. This is promising evidence for probing how candidates actually move metrics and what they learn—not proof that the round is a reliable predictor.
The other scored rounds show no positive relationship with ratings in this small hired sample:
| Round | Association with 12-month rating among hires |
|---|---|
| --- | ---: |
| Take-home | r ≈ −0.50 |
| Presentation | r ≈ −0.85 |
| Behavioural metric | r ≈ +0.93 |
| VP Engineering | r ≈ −0.79 |
The negative estimates for presentation and VP Engineering should not be read as evidence that those skills hurt PM performance. With only eight hires, these figures are highly unstable. They do tell us that our current scores offer no reassuring evidence that those rounds predict success.
How confident we should be
Low confidence overall; modest confidence that the behavioural round is the best lead to investigate. Eight outcomes are far too few to set reliable weights or cutoffs, and interview scores are ordinal ratings, so the correlations are descriptive only. We also observe performance only for people we hired: panel decisions restrict the range of scores and can distort associations. Non-hires have no performance outcome for comparison.
We lack recruiter-screen scores, interviewer-level data, consistent evidence of scoring reliability, and context about role, manager, or rating differences. The company-average comparison is also a coarse measure. The data can guide a better experiment; it cannot establish causation or validate the loop.
Loop I’d run next quarter
- Keep the recruiter screen for eligibility, motivation, and logistics, but don’t treat it as a predictive assessment. We have no screen-score data to evaluate.
- Replace the week-long take-home deck and separate presentation with one time-boxed, job-relevant work sample. Use a live or short-prep product problem, not a polished deck. Score problem framing, user evidence, prioritization and tradeoffs, success measures, and communication against anchored criteria. This reduces candidate burden and stops us treating two related presentation exercises as independent evidence.
- Keep and strengthen the behavioural round. Ask for a specific metric the candidate owned or influenced; probe the baseline, their actions, evidence of impact, attribution, tradeoffs, and what they learned. Use the same questions and anchored scorecard for every candidate.
- Refocus the VP Engineering conversation on PM–engineering collaboration and concrete technical tradeoffs—not general “technical credibility.” Use a structured scenario and score it consistently; don’t make an unstructured impression a veto.
- Have interviewers score independently before the panel discussion. The hiring manager can decide after reviewing evidence against the role criteria, with any disagreement recorded. Do not introduce numerical weights based on these eight hires.
For each of the four hires, preserve round-level scores and interviewer notes, then collect comparable 6- and 12-month outcomes with role and manager context. Review the evidence after the cohort has matured. Next quarter’s loop should be a more structured, lower-burden test—not a claim that we have already found a validated predictor.
Grades and run details
Decision model 83 · LLM judge 10 of 13 checks
Decision model checks
- failUses the supplied evidence correctly21%
- passAddresses the actual decision61%
- passRespects explicit constraints50%
- passIdentifies material uncertainty77%
- passAvoids unsupported claims59%
- passProduces the required deliverable64%
- passFinds which rounds line up with performance92%
- passSays how little nine hires can show99%
- partialSpots the interviewer pattern86%
- passJudges on evidence, not presence97%
- partialDefines good for this role first28%
- passKeeps each judgement independent97%
Run
- Run
- #1
- API response time
- 33 s
- Submitted
- 5 Oct 2026