Hire a PM

MEMORANDUM

TO: Elif Demir, VP of Product FROM: Director of Product DATE: October 24, 2023 SUBJECT: PM Interview Loop Analysis & Recommendations for Q1 Hiring

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Elif, you are entirely right to flag this. Looking back at our last 18 months of hiring data, out of our 6 hired PMs who completed their first year, half (C107, C132, C148) are struggling with 12-month performance ratings of 2 (well below our company average of 3.4). Meanwhile, our top performers (C101, C141 with ratings of 5, and C149 with a rating of 4) represent the standard we actually need.

To prep for hiring four more PMs next quarter, I’ve analyzed the data for all 52 candidates who reached our final rounds. Here is what works, what is completely broken, how confident we can be, and how we should redesign the loop.

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1. What Predicts Performance? (And What Doesn't)

By analyzing the correlation between interview stage scores and 12-month post-hire ratings for our 6 completed hires, we see stark contrasts in signal quality:

* Take-Home Strategy Deck (Zero to Negative Correlation): Our take-home exam is a massive waste of candidate time and offers zero predictive value. Our struggling hires (C107, C132, C148) averaged a 3.7 out of 4 on the take-home. Conversely, our top performers averaged a 2.7. High scores on a 1-week unsupervised deck merely signal that a candidate has free time, a supportive current employer, or an agency background—not that they can execute at Kestrel. * Take-Home Presentation Panel (Weak / Misleading Signal): Similar to the take-home itself, presentation scores do not align with success. C107 and C148 both scored 4s on their presentation and turned out to be 12-month "2" ratings. * VP Engineering Conversation (Inverted Signal): Surprisingly, the VP Engineering score shows a negative relationship with success. Candidates who scored high with Engineering often struggled on the job, while our top performers (C101, C141, C149) mixed moderate-to-low engineering scores (2 to 3) with high marks elsewhere. While technical credibility matters, our current technical screen appears to favor academic/theoretical answers over pragmatic product-engineering partnership. * Behavioural Metric Round (Our Only Real Signal): The behavioural round ("Tell me about a metric you moved") is the only component in our current loop that correlates with on-the-job success. * Our three struggling hires averaged 2.0 on this round. * Our three successful hires averaged 4.0 on this round. This is our sole bright spot. It tests past execution, ownership, and learning—things that actually matter day-to-day at Kestrel.

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2. Statistical Confidence

We must temper our confidence. We have only 6 data points of complete hire-and-tenure data. While the directional trend on the behavioural round is unmistakable (+2.0 delta between high and low performers), running statistical significance tests on $n=6$ is risky.

However, we can look at broader hiring funnel data across all 52 candidates to understand drop-offs. Notably, 13 candidates withdrew from the process, heavily skewed toward candidates who scored well on the take-home (e.g., C100, C113, C142, C147 all scored 4s on the take-home and walked away). Our heavy, one-week take-home is actively driving away top-tier passive talent who refuse to do free consulting work for a week, leaving us to select from a pool of people willing to endure it.

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3. The Proposed Loop for Next Quarter

To scale our team to four new PMs without repeating past mistakes, we need to strip away vanity metrics (the take-home deck) and lean heavily into what actually predicts success: rigorous behavioural assessment of past execution, combined with a real-time collaborative product session.

Here is the 4-stage loop I propose we deploy immediately:

1. Recruiter Screen (30 mins): Standard fit, compensation alignment, and basic background check.
2. Product Craft & Execution Interview (45 mins - Live Video): Replaces the take-home. Instead of a week-long deck, we give the candidate a real Kestrel product challenge 48 hours in advance (requiring no more than 2 hours of prep) and spend the interview deep-diving into their hypotheses, prioritization trade-offs, and customer empathy.
3. Behavioural & Metric Ownership Round (45 mins): Double down on what works. Retain our behavioural round focusing on metrics moved, but expand the rubric to explicitly test for accountability, cross-functional conflict resolution, and handling failure.
4. Product-Engineering Partnership Round (45 mins): Refocused Engineering screen. Instead of "technical credibility" evaluated in a vacuum, this is a joint session with the VP of Engineering or a Senior Tech Lead to evaluate how the PM collaborates with engineering to ship product, scope down MVPs, and manage technical debt.

Decision Gate: Drop the consensus panel meeting where the loudest voice wins. The hiring manager makes the final call, but only if the candidate scores a predefined threshold on the Behavioural and Product Craft rounds.

Summary

By eliminating the 1-week take-home, we will stop scaring away top-tier talent, shorten our time-to-hire, and refocus our interview panel on real-world execution rather than polished slide decks. Let me know if you are aligned, and I will draft the new interview rubrics by Friday.

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 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:

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.