Hire a PM

To: Elif Demir, VP Product From: Director of Product Re: What our PM loop predicts, and the loop I'd run next quarter

Bottom line

The behavioural metric round is the only part of the loop that tracks how hires perform. The presentation and VP Engineering rounds seem to drive hiring decisions without predicting performance. All three hires who struggled scored 4 on VP Engineering and 2 on the metric round. I'd make the metric round the centre of the loop and stop weighting the others as heavily. The sample is small, so this is a strong hypothesis to test, not a settled answer.

What the data covers

• 52 candidates reached the final rounds over 18 months: 9 hired, 33 rejected, 10 withdrew.
• Only the 9 hires have 12-month ratings. We can't tell whether our rejections were right, so we can't measure false negatives.
• We don't record withdrawal stage or reason, or which interviewer gave each score, so we can't check consistency across panels.

What the data shows

Specifics:

• Metric round: Hires who scored 2 all rated 2 at 12 months (3 of 3). Hires who scored 3 or 4 averaged 4.0. Its average gap between hired and rejected candidates is the smallest of the four, so the panel leaned on the other rounds more.
• The three struggling hires (rated 2 after 12 months) all scored 4 on VP Engineering and 4 on presentation, and scored 3–4 on the take-home. They looked strong everywhere except the metric round, which was the signal that predicted their problems.
• VP Engineering: All four hires who scored 4 here were rated 2 or 3 at 12 months.
• Withdrawals: 8 of the 10 candidates who withdrew scored 3 or higher on the metric round. We are losing strong candidates, and we don't know where or why.
• Background (hypothesis only): Big-tech hires (4) averaged 2.25 at 12 months; startup hires (3) averaged 4.7. The numbers are far too small to act on, but we should track it.

How confident we should be

• Metric round as the best predictor: moderate. A correlation of about 0.9 across nine hires is striking, and it fits what PM work involves: diagnosing a metric and deciding what to do about it. But with nine people, one or two different ratings would move the result a lot.
• Negative readings for presentation and VP Engineering: low. We only see hires, so the range is restricted, and the 1–4 scale gives few distinct values. The defensible claim is that these rounds did not predict success. I wouldn't claim they predict failure.
• Take-home as a screen-out: low to moderate. Zero hires among ten candidates who scored 1 is consistent with the take-home filtering out weak work, but that's a small group.
• Anything about rejected candidates: no confidence. We have no data on them.

The loop I'd run next quarter

1. Recruiter screen: No change.
2. Take-home: Keep it as a gate. Candidates who score 1 don't advance without a written override. Cap the time we ask for, since we're losing strong candidates and may be losing some to the length of the process.
3. Metric round (anchor): Rewrite as a structured 60-minute session with two interviewers. Candidates diagnose a real metric movement from a dashboard, propose causes, and say what they'd measure next. Score against a written rubric on 1–4 scales. A score of 3 or higher is required to hire. A score of 2 or lower is a no-hire unless you sign a written override.
4. Presentation: Fold it into the metric round as a communication check, so candidates spend less time on it, and don't weight it separately in the decision.
5. VP Engineering conversation: Keep it for a pass/fail check on serious collaboration red flags. Don't score it or count it in the decision, given how it has tracked performance.
6. Decision process: Every interviewer submits written scores before the panel meets, so no one anchors on the first opinion. The hiring manager still decides, but must write down why if they hire against the metric-round rule or reject someone who met it.
7. Data: Record the interviewer for every score, withdrawal stage and reason, and 6- and 12-month ratings for every hire. Keep scoring the old rounds next quarter even though they won't be weighted, so we keep building the dataset.

What I need from you

• Approval for the metric-round rubric and the override rule.
• Agreement on a review date. Four hires next quarter won't settle this. I'd treat the next two quarters as calibration, with a 6-month review of the four new hires and a full review once we have about twelve hires on the new loop.
• A decision on whether to pay for a small outside check on our rubric, such as having a PM coach double-score a sample of recordings.

To: Elif Demir, VP Product From: Director of Product Re: What our PM loop predicts, and the loop for next quarter

Summary

Of the five stages in our loop, only the behavioural "metric you moved" round tracks how hires perform after 12 months. The take-home, the presentation and the VP Engineering conversation all point the wrong way: hires who scored higher on them performed worse. The evidence is thin (nine hires), so I'd treat this as a strong hint rather than proof. Even so, it is consistent enough to change the loop now and to measure the new loop properly.

What the data shows

Over 18 months, 52 candidates reached the final rounds. We hired 9, rejected 33, and 10 withdrew. Our hires averaged 3.3 at 12 months, slightly below the PM average of 3.4. The three who struggled are C107, C132 and C148, all rated 2.

Average 12-month rating of hires, by interview score

The three strugglers share an identical profile:

• 4 on the presentation
• 4 on VP Engineering
• 3 or 4 on the take-home
• 2 on the behavioural round
• Big-tech background

Our two strongest hires, C101 and C141 (both rated 5), had middling presentation and VP Engineering scores but scored 4 on the behavioural round.

In other words, we have been hiring for polish and for technical fluency, and overriding the one signal that worked.

Background is tangled up in this. All four big-tech hires were rated 2 or 3, averaging 2.25. The five others averaged 4.2. The VP Engineering round looks especially exposed to this:

• Every VP Engineering score of 4 in the dataset went to a big-tech candidate (9 of 11 big-tech candidates).
• No other candidate scored 4.

The round may be measuring familiarity with big-tech engineering culture rather than how well someone will work with our engineers.

The behavioural result holds within each group. This is the most reassuring part of the analysis.

• Among non-big-tech hires, the behavioural score still tracks performance. Those who scored 4 were rated 5, 5 and 4. Those who scored 3 were rated 3 and 4.
• Among big-tech hires, the one who scored 3 was rated 3 and the three who scored 2 were rated 2.

So the behavioural signal isn't simply a stand-in for background. Within non-big-tech hires, the presentation and take-home scores still lean negative.

How confident we can be

I'm only moderately confident, for five reasons:

• Nine data points. One or two different outcomes could change the picture, and none of these correlations would survive a strict significance test.
• We only see outcomes for people we hired. Hires were selected partly on high presentation and VP Engineering scores, so we can't see how low scorers on those stages would have done. We also don't know about strong behavioural candidates we rejected (C121, C123, C131, C135, C146), or about withdrawals. C135, for example, was rejected despite scoring 3, 4, 4 and 3, among the strongest sets of scores in the pool.
• Background is confounded. With four big-tech hires, I can't fully separate "big-tech PMs struggle here" from "our loop rewards the wrong things in big-tech PMs." Either way, the fix is to the instruments, not to filter on background.
• 12-month ratings are one manager's judgement, and they also reflect team and scope.
• Interviewer effects. We don't know who scored which candidates, so some of this may come from individual raters rather than from the stages themselves.

What I am confident about is narrower. The take-home, presentation and VP Engineering scores give us no evidence that they help. Given what they cost candidates and the panel, that is reason enough to stop relying on them.

The loop I'd run next quarter

1. Recruiter screen. Unchanged.

2. Structured behavioural round, now the core of the loop (two interviewers, about 60 minutes). - Keep "a metric you moved," and add a second prompt: "a decision you got wrong and what you changed." - Use a written rubric that scores: - specificity about the candidate's own actions; - how they diagnosed the problem; - the trade-offs they made; - what they learned. - Score the substance, not the delivery. Each interviewer scores independently.

3. Replace the week-long take-home and presentation with a 90-minute live working session. - Give the candidate a real, anonymised Kestrel problem with messy data, and work through it with a PM and an engineer. - Score how they reason, prioritise and respond to pushback, not how finished the output looks. - This also cuts the candidate time we ask for. Ten of 52 finalists withdrew; I can't say the week-long take-home caused that, but it won't have helped.

4. Redesign the VP Engineering round as a structured "working with engineering" round. - Base it on a scenario such as a scope cut, a technical-debt trade-off or an estimate dispute, with a rubric agreed with Engineering. - Score it pass/concern only until we have evidence that it predicts anything, so it can flag real problems without rewarding big-tech vocabulary.

5. Change how we decide. - All scores and written evidence go in before the debrief, so nobody anchors on the loudest voice. - No hire with a behavioural score below 3. - The hiring manager still decides. If they hire against the behavioural score, they write down why.

6. Measure it this time. - Log every interviewer's scores, not just the panel's. - Collect 6-month and 12-month ratings against a consistent rubric. - With four more hires we'll have 13 to analyse. We should review the data in two quarters, not two years. - I'd also like a quick look back at the strong behavioural candidates we rejected, to see whether the debrief was systematically discounting that round.

The ask

I'd like your sign-off to:

• pilot this loop for the four upcoming hires;
• get two hours of interviewer calibration on the new rubrics;
• secure the VP Engineering's agreement to redesign their round.

I can have the rubrics and the working-session problem ready within three weeks.

To: Elif Demir, VP Product From: Director of Product Subject: PM interview loop: what the evidence supports and what to change next quarter

Recommendation

For the next four hires, make demonstrated product judgment, metric ownership, and cross-functional execution the center of the loop. Replace the week-long strategy deck and separate presentation with a short, standardized work sample; retain and strengthen the behavioral metric interview; replace the unstructured “technical credibility” conversation with a scored collaboration exercise.

These are evidence-informed changes, not a validated hiring formula. Our strongest signal comes from just nine hires.

What appears to predict performance

We have 52 finalists: nine hired, 33 rejected, and ten withdrawn. Only the nine hires have performance outcomes. Their average 12-month rating is 3.33, versus the company PM average of 3.4. Three received a 2; none received a 1. The similar averages do not negate the three struggling hires, but neither establish that the entire loop is failing.

Among hires:

Descriptive Pearson correlations on ordinal scores, based on nine hires—not validated predictive estimates.

The behavioral round shows a particularly clear pattern: the three hires scoring 2 all subsequently received performance ratings of 2; those scoring 3 averaged 3.33; those scoring 4 averaged 4.67. It plausibly tests something relevant to the job: whether candidates can explain their own contribution, reason about outcomes, and learn from results.

Conversely, three hires—C107, C132, and C148—received presentation and VP Engineering scores of 4, but later performance ratings of 2. C101 scored 2 in both those rounds and subsequently received a 5. We should stop treating polish or this version of technical credibility as sufficient evidence of PM effectiveness.

We have no scored evidence for the recruiter screen, and cannot assess the independent value of panel discussion or hiring-manager judgment from these files.

How confident should we be?

Confident about the observed pattern; low confidence that it will generalize.

• Nine outcomes are too few to estimate stable predictive relationships or optimize weights and cutoffs. A few cases could materially change the results.
• Selection limits the analysis. We observe performance only for people our existing process hired. Rejected and withdrawn candidates are not failed PMs; their missing outcomes prevent us from measuring false negatives or assessing validity across all finalists.
• Background and scores are entangled. The four big-tech hires averaged 2.25 in performance; the three startup hires averaged 4.67. Background also overlaps with interview-score patterns. We cannot isolate interview effects from background, assignments, support, or other differences—and should not turn this into a background preference.
• The rounds are not independent. The deck and presentation share content, while ratings may reflect overlapping impressions. We cannot establish which round adds value beyond another.
• Performance ratings are an imperfect criterion. We lack role scope, manager, onboarding, and rating-calibration information. The company average is not a matched comparison group.

Negative correlations therefore do not mean we should prefer poor presenters or candidates with low technical scores. They mean the current versions have not demonstrated useful positive signal.

The loop I would run next quarter

1. Structured recruiter screen. Confirm role expectations, relevant experience, motivation, and logistics using consistent questions. Explain the process and preparation requirements upfront.

2. Standardized product work sample, replacing the deck and presentation. Use a fictional product brief and small dataset, with a roughly 60–90-minute total candidate commitment. Ask candidates to diagnose a problem, choose an outcome metric, prioritize options, and explain tradeoffs. Introduce a new constraint during discussion to test adaptation, not slide polish. Use equivalent prompts, consistent preparation conditions, and accommodations where needed.

3. Structured behavioral metric interview. Keep the strongest observed signal. Probe the baseline, candidate’s personal actions, alternatives considered, attribution versus correlation, unintended effects, and lessons learned. Score ownership, analytical judgment, and learning separately using behaviorally anchored rubrics.

4. Structured engineering collaboration interview. Replace the standalone credibility conversation with a realistic scenario involving delivery constraints, instrumentation, and competing priorities. An engineering interviewer scores clarification, tradeoffs, and collaboration—not jargon, pedigree, or conversational rapport.

5. Evidence-led decision. Set role-specific competencies and minimum requirements before interviewing. Interviewers submit independent scores and concrete evidence before discussion. The hiring manager documents the decision and any override. Presentation polish must not compensate for missing evidence on critical competencies. Do not derive mechanical cutoffs from these nine hires.

How we will know whether this is better

Preserve round-level scores, interviewer identities, decisions, overrides, withdrawals, and candidate feedback. Define consistent, role-adjusted performance measures at 3, 6, and 12 months; where feasible, have reviewers assess performance without seeing interview scores.

Review candidate burden and scoring consistency immediately. Treat outcomes from the next four hires as additional evidence—not proof of success. Build a larger cohort before claiming predictive validity or tuning numerical weights.