Interview guides · KoBold Metals

KoBold Metals Data Scientist Interview Questions & Guide 2026

KoBold Metals data scientist prep on data pitfalls: selection bias, target leakage, and checking an outlier's provenance before you drop it.

The 60-second brief

The Data Scientist role at KoBold Metals is integral to the company's mission of revolutionizing mineral exploration through innovative data-driven approaches. As a Data Scientist, you will leverage vast amounts of geospatial and geological data to derive insights that influence decision-making in mining exploration and resource management. Your work will directly impact the efficiency and accuracy of locating valuable mineral deposits, which is critical for the company's sustainability and operational success.

Seniority shifts the scope more than the words in the title do. Earlier-career loops mostly check that you execute a well-posed analysis correctly; senior loops check that you can decide which question is worth answering and defend what you chose not to do.

Focus areas

Size an experiment before anyone launches it
Decompose a metric move by segment and mix
Pick a randomisation unit that respects interference

Overview

In this role, you will collaborate with multidisciplinary teams, including geologists, engineers, and product managers, to develop predictive models and analytical tools. You will be engaged in complex problem-solving tasks that require both technical expertise and a thorough understanding of geological processes. This position is not only about analyzing data but also involves translating findings into actionable strategies that enhance project outcomes and drive business growth. The challenges you will face are diverse and will require a blend of creativity, technical knowledge, and a passion for advancing the mining industry's capabilities.

The KoBold Metals interview process

KoBold Metals candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

HR Screening

Whoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.

What to demonstrate

Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
Whether your reason for wanting the role points at the work itself rather than the company's reputation
Whether your language signals the level being screened for: what you decided yourself versus what you were handed

How to prepare

Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands

Technical Assessment

Before anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.

What to demonstrate

Whether an ambiguous term becomes a specific column and filter before any computation happens
Whether you read the schema for keys and cardinality rather than only for column names
Whether the result answers the question at the grain it was asked at, per user or per session or per day

How to prepare

Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over

Team Lead Discussion

An added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.

What to demonstrate

Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact

How to prepare

Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.

Senior Executive Interview

Because the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.

What to demonstrate

Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs

How to prepare

For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.

Where candidates lose points

Slicing a flat experiment until a segment reaches significance

Testing one metric across twenty segments at a nominal 5% level produces a significant result about two thirds of the time when nothing is happening anywhere, and the segment that surfaces is by construction the one with the most favourable noise. The reported effect in that slice is then badly overstated, because selection on significance conditions the estimate on being large. What makes it dangerous rather than merely wrong is that a post-hoc segment always has a plausible story attached, so it survives the meeting. The controls are declaring the small number of segments of interest before launch, correcting across the ones tested, and treating anything discovered afterwards as a hypothesis that needs its own adequately-powered test rather than a finding.

Reading a pooled rate that moved because the mix moved, not because any behaviour changed

A pooled conversion rate is a weighted average, and a shift in the weights can move it in the opposite direction to every one of its parts. A paid campaign that brings low-converting traffic drops overall signup conversion even if desktop, mobile web and app conversion each rose that week, which is Simpson's paradox and it is the single most common cause of an inexplicable dashboard move. The discipline is to decompose before explaining: recompute the rate holding last period's segment weights fixed, and compare that counterfactual to the actual, so the mix effect and the rate effect are separated numerically rather than argued about. Segment on the dimensions that actually reweight, which in this domain are almost always device_type, referrer_channel, country and new versus returning.

Sizing estimates built on unnamed, unrevisable assumptions

Write each assumption as a named number you can change, then show the arithmetic so the interviewer can challenge one input instead of the whole answer. Finish by saying which assumption the result is most sensitive to, which matters more than the point estimate.

Analysing at a different unit than the one randomised

Say out loud what was randomised (user, device, account, cluster) and make the analysis unit match, or account for the clustering with cluster-robust standard errors, the delta method, or aggregation up to the randomised unit. Randomising users and then running a test over sessions understates variance and inflates the false-positive rate.

Questions & practice

Explain your experience with machine learning algorithms and when you would use each.

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Check what information would not exist at prediction time, and exclude it.
Set a baseline first, so any model has something honest to beat.

Follow-up

Where could label leakage enter this setup?
How would you choose the decision threshold, and who owns that choice?

Implement a basic clustering algorithm and explain its application in mineral exploration.

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Frame the prediction: the label, the moment of prediction, and the action it triggers.
Set a baseline first, so any model has something honest to beat.

Follow-up

How would you choose the decision threshold, and who owns that choice?
Where could label leakage enter this setup?

Split a pooled conversion drop into rate and mix

Medium · mix shift · decomposition · pandas

You have weekly visit-to-signup counts by segment: a DataFrame with week, device_type, referrer_channel, visitors and signups. The pooled rate fell 0.84 percentage points between two consecutive weeks while several individual segments rose. Write a function that, for a caller-supplied list of segment columns, splits the pooled change into a rate effect, a mix effect and an interaction term that sum exactly to the observed change. Return those three scalars plus a per-segment contribution table sorted by absolute contribution, so the largest single driver can be named.

Approach

State the algebra before coding: the pooled rate is r = sum over segments of w_s * r_s, with w_s the segment's share of the denominator. Then r1 - r0 decomposes exactly into sum(w_s0 * (r_s1 - r_s0)) for rate, sum((w_s1 - w_s0) * r_s0) for mix, and sum((w_s1 - w_s0) * (r_s1 - r_s0)) for interaction. The identity is per-segment, so it holds for any numbers you put in the four slots.
Pivot both weeks onto a common segment index with an outer join so a segment that appeared or vanished is kept rather than dropped, then decide what rate to give a segment with no visitors in one of the weeks, and document the choice. The identity stays exact either way because the missing week's weight is 0, but the attribution does not. Filling the missing rate with 0 sends an appearing segment's entire w_s1 * r_s1 into the interaction term, since w_s0 = 0 makes both the rate term and the mix term (w_s1 - w_s0) * r_s0 identically zero; a vanishing segment then splits as -w_s0 * r_s0 in rate, -w_s0 * r_s0 in mix and +w_s0 * r_s0 in interaction.
The convention used below instead imputes the missing week's rate as that week's pooled rate. A vanishing segment then lands wholly in mix at -w_s0 * r_s0, with rate and interaction cancelling; an appearing segment puts w_s1 * r_pooled0 in mix (volume arriving at the average rate) and only w_s1 * (r_s1 - r_pooled0) in interaction (its rate differing from that average). Impute by which week the segment is missing from, never by argument order, or the swap identities below stop holding.
Guard the division where visitors is 0 so no NaN enters the vectors, because a single NaN poisons every sum. A segment with zero visitors in both weeks contributes exactly 0 and can be dropped; a segment missing from only one week does not contribute 0, and where its contribution lands is settled by the convention above, not by the guard.
Compute the three components as vectors over segments, then sum. Keep the vectors, because the per-segment contribution table is what turns the decomposition into an explanation.
Assert that the three components sum to the observed pooled change within floating-point tolerance. This identity is exact, so a mismatch means an implementation bug, not a modelling judgement.

Worked solution (25 min)

Aggregate to one row per (week, segment tuple) with summed visitors and signups, then split into w0 and w1 frames and align with an outer join, filling missing visitors and signups with 0.
Compute w_s = visitors / visitors.sum() within each week, and r_s = signups / visitors only where visitors > 0. Where a week's visitors are 0, set that week's r_s to that week's pooled rate, the stated convention; never leave it NaN.
rate_effect = (w0 * (r1 - r0)).sum(); mix_effect = ((w1 - w0) * r0).sum(); interaction = ((w1 - w0) * (r1 - r0)).sum().
contribution = w0*(r1-r0) + (w1-w0)r0 + (w1-w0)(r1-r0) per segment, which reduces to w1r1 - w0r0; sort by abs and return the head.
assert abs(rate + mix + interaction - (pooled1 - pooled0)) < 1e-12.

Expected result: Three scalars summing exactly to the observed pooled change, plus a segment table whose contribution column also sums to that change. In the stated case rate and mix carry opposite signs: several segments converted better while volume moved toward a weaker-converting one, which is what makes the pooled move look inexplicable until it is split.

Verify it yourself

On synthetic data where every segment appears in both weeks, every segment rate is held constant and only the weights move, rate_effect and interaction are exactly 0.
On synthetic data where every segment appears in both weeks, weights are held constant and only rates move, mix_effect and interaction are exactly 0.
The per-segment contribution column sums to the pooled change, matching the sum of the three scalars.
Swapping the two weeks negates the total, as it must, but not the parts. Assert the three identities that actually hold: the interaction term is exactly invariant, because both of its factors change sign; the swapped rate effect equals -(rate_effect + interaction); and the swapped mix effect equals -(mix_effect + interaction). They still sum to the negated pooled change. Expecting all three to flip sign is the common wrong assertion.
Plant a segment present only in week 1, holding 3 percent of week-1 visitors at a rate well below pooled. Under the documented pooled-rate convention its mix contribution is w_s1 * r_pooled0 and its interaction contribution is w_s1 * (r_s1 - r_pooled0), with a rate contribution of exactly 0; under a zero-fill convention its mix contribution is exactly 0 and the whole w_s1 * r_s1 lands in interaction. Assert whichever one the function documents, and fail if mix is asserted non-zero while the code zero-fills.

Follow-up

The mix effect accounts for 0.71 of the 0.84 point drop, driven by paid_social volume. What is your recommendation, and what would change it?
Why is a two-way split into a counterfactual rate and a residual also exact, and when would you prefer it to the three-way version?
Segmenting on device and channel leaves a large interaction term. What does that tell you about the choice of segments?

Write a function that identifies anomalies in a dataset.

Medium · sql and data manipulation

Approach

Check whether any join is one-to-many before aggregating, or the sums inflate.
Compute rates by summing numerator and denominator separately, never by averaging rates.
State the window function and its partition and ordering out loud before writing it.

Follow-up

What breaks if events arrive late or out of order?
How does the query change if the join becomes one-to-many?

Seven-day activation rate by weekly signup cohort

Easy · cohorts · activation · date-bounds

dim_user holds user_id, account_created_at_utc, is_internal. fct_event holds user_id, occurred_at_utc, is_core_action. A user is activated when core-action events fall on at least two distinct UTC dates inside [account_created_at_utc, account_created_at_utc + 7 days). Return, for the last twelve complete weekly signup cohorts, the cohort week, cohort size, activated users and the activation rate. Exclude is_internal users. Every signup in the cohort week stays in the denominator, including users who never returned.

Approach

Start from dim_user as the denominator spine with is_internal = FALSE and DATE_TRUNC('week', account_created_at_utc) as the cohort key. Driving the query from the event table instead would silently condition on having events and delete the entire non-activating population.
Join fct_event on user_id with is_core_action = TRUE and a per-user bound, occurred_at_utc >= u.account_created_at_utc AND occurred_at_utc < u.account_created_at_utc + interval '7 days'. The bound is correlated to each user's own signup timestamp, not a single global date range.
Aggregate per user with COUNT(DISTINCT occurred_at_utc::date) >= 2, then LEFT JOIN that back onto the spine and COALESCE the flag to FALSE so non-activators contribute a zero rather than vanishing.
Restrict the published cohorts to those whose week ended at least eight days ago. A cohort younger than that has not finished its seven-day window, so its rate is mechanically low and reads as a decline.
Roll up by summing the numerator and denominator per cohort week, and state the two-distinct-days threshold next to the number since it is a choice that re-bases the whole history if changed.

Worked solution (20 min)

Write the cohort spine and confirm its total equals the count of non-internal signups in the date range.
Write the per-user distinct-active-days CTE with both interval bounds and inspect a handful of users manually.
LEFT JOIN, COALESCE the flag, aggregate to cohort week.
Apply the eight-day publication lag and drop the incomplete cohort.
Re-run with a closed upper bound (<= +7 days) and note how many users change state, to show the boundary is doing work.

Expected result: Twelve rows, one per cohort week, each with cohort_size, activated and rate. Cohort sizes sum to the total non-internal signup count across those twelve weeks. No cohort is younger than eight days.

Verify it yourself

activated is never greater than cohort_size in any row.
A user with three core actions on a single day is not counted as activated.
Removing the LEFT JOIN in favour of an inner join raises every rate, which demonstrates the denominator was being conditioned on survival.
Cohort sizes sum to an independent COUNT(*) over dim_user for the same range.

Follow-up

Why two distinct days rather than one event? What happens to the published history if someone changes it to three?
Invited seats and SSO-provisioned users get an account_created_at_utc at provisioning and may never sign in. Should they be in this denominator?
The rate rose 3 points this week. What do you check before believing it?

How do you prioritize tasks when managing multiple projects?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
Name one primary metric, then the guardrail that stops it being gamed.
Fix the population and the time window before naming any metric.

Follow-up

Which segment would you cut first, and what would that rule out?
What would you do if the primary metric and the guardrail moved in opposite directions?

How would you optimize a data pipeline for processing large geospatial datasets?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
Fix the population and the time window before naming any metric.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

What would you do if the primary metric and the guardrail moved in opposite directions?
How would you detect that the metric is being gamed rather than genuinely improving?

If tasked with optimizing a mining operation using predictive analytics, how would you approach the problem?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
Restate the decision this analysis has to support, and who acts on the answer.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

What would you do if the primary metric and the guardrail moved in opposite directions?
Which segment would you cut first, and what would that rule out?

Given a dataset of mineral exploration results, what steps would you take to analyze the data and present findings?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
Decompose the metric into the rates that drive it, and say which one you would check first.
Restate the decision this analysis has to support, and who acts on the answer.

Follow-up

Which segment would you cut first, and what would that rule out?
What would you do if the primary metric and the guardrail moved in opposite directions?

Describe a project where you applied data analysis to a real-world problem.

Medium

Approach

Work from the decision backwards to the evidence you would need.
Clarify what is being asked and what a complete answer would contain.
State your assumptions explicitly before working the problem.

Follow-up

How would you know your answer was wrong?
What assumption would you test first?

Size an activation test before anyone writes code

Easy · power · minimum detectable effect · proportions

A team wants to test a new onboarding checklist against the seven-day activation rate: users with is_core_action = TRUE events on at least two distinct UTC dates inside the first seven days, counted from dim_user.account_created_at_utc with is_internal = FALSE. Baseline is 22%. Weekly non-internal signups are 9,000, split evenly between two arms. State the minimum detectable effect after two weeks of enrolment, the enrolment needed to detect a 1.5 percentage point absolute lift, and the earliest date a readout would be honest.

Approach

Use the two-proportion sizing shortcut n = 16 p(1-p) / d^2 per arm, where d is the absolute lift. The 16 is 2(1.96 + 0.84)^2 = 15.7 rounded, i.e. two-sided alpha 0.05, 80% power, equal arms.
Invert it for the MDE at fixed n: d = sqrt(16 p(1-p) / n). Two weeks gives 9,000 per arm, so d = sqrt(2.746 / 9000) = 0.0175, which is 1.75 percentage points absolute or 7.9% relative on a 22% base.
Run it forward for d = 0.015: n = 2.746 / 0.000225 = 12,202 per arm, 24,404 total, about 2.7 weeks of enrolment. Round up to three whole weeks so the enrolment window contains no partial week, since signups are not uniform across weekdays.
Add the metric's own lag rather than reporting the enrolment end date. Activation is unobservable until seven days after the last enrolled signup, and the definition publishes on an eight-day lag, so the readout lands 29 days after launch.
Close by testing whether the ask is realistic. If the team's honest prior is a 0.5pp lift, the requirement is about 110,000 per arm and roughly 24 weeks of enrolment, and saying so before launch is the deliverable.

Worked solution (20 min)

Compute p(1-p) = 0.22 x 0.78 = 0.1716 and 16 x 0.1716 = 2.746.
MDE at two weeks: n = 4,500 per arm per week x 2 = 9,000; d = sqrt(2.746 / 9000) = 0.0175.
Sample for d = 0.015: n = 2.746 / 0.000225 = 12,202 per arm, which is 2.71 weeks of enrolment, so enrol three whole weeks.
Readout date = 21 days of enrolment + 8 days of follow-up and publication lag = 29 days from launch.
State the boundary of what this quarter can answer: below roughly 0.7pp the requirement passes 56,000 per arm and more than a full quarter of enrolment.

Expected result: MDE after two weeks is 1.75 percentage points absolute, 7.9% relative. Detecting 1.5pp needs 12,202 users per arm, so three weeks of enrolment and a readout 29 days after launch. Detecting 0.5pp needs about 110,000 per arm, roughly 24 weeks, which is the answer the team needs to hear before the code is written.

Verify it yourself

MDE must fall as 1 / sqrt(n). Doubling enrolment from two weeks to four should move 1.75pp to 1.24pp, not to 0.88pp.
Substituting d = 0.0175 back into 16 p(1-p) / d^2 must return about 9,000 per arm.
An exact two-proportion calculator at alpha 0.05 two-sided, power 0.80, p1 = 0.22, p2 = 0.235 returns about 12,260 per arm, within half a percent of the shortcut.

Follow-up

The team also wants trial-to-paid read from the same test. What does a second primary metric do to your alpha, and what would you actually do about it?
Signups are heavier on weekdays. What goes wrong if enrolment runs 17 days instead of 14?
The activation definition uses a two-distinct-days threshold. If that were loosened to one day, what happens to the baseline rate and to the sample you need?

Size every candidate cause of a trial-to-paid decline

Hard · cohort-maturity · experiment-readout · mix-shift · contribution-sizing

Trial-to-paid conversion on weekly trial-start cohorts from fct_subscription_period reads 3.1 points below the trailing eight-week mean for the three most recent cohorts. Three things happened in that window: a pricing experiment reached 50% of new trials, a payment processor migration added settlement delay, and paid_search spend tripled. Using fct_subscription_period, fct_experiment_exposure and dim_user, rank the causes by their contribution in points of the headline, state the remainder, and give the decision you would take on Monday.

Approach

Kill the immature cohorts first, because everything downstream is computed on them. The metric is lagged by the trial length plus a 14-day conversion window plus a settlement allowance, and a processor migration lengthens exactly that last term; recompute each cohort at a fixed cohort age rather than as of today, and confirm the newest cohort's value is still climbing day over day.
Hold the experiment analysis to the exposed population. Join fct_experiment_exposure on unit_id with is_in_analysis_population = TRUE rather than reading an assignment log, then check the variant split for a sample-ratio mismatch before believing any effect at all. Contribution to the headline is the variant effect multiplied by the exposed share, which is not the same number as the variant effect.
Decompose the cohort mix by dim_user.first_touch_channel using the same weight-times-rate arithmetic as any other mix question, so the paid_search increase is sized as a weight change at a measured conversion rate rather than asserted from the spend figure.
Convert all three to points of the headline, sum them, and print the residual against the historical week-to-week standard deviation of the metric. If the residual is inside that band, say so and stop looking; if it is outside, name what you would investigate next rather than leaving it implied.
Land the decision. Only one of the three is actionable on Monday, so state whether the experiment has accrued enough exposed units to stop at the pre-declared horizon, and state separately what the settlement-lag correction does to the published series and its lag rule.

Follow-up

How do you choose the fixed cohort age, and what do you lose by choosing it too long?
If the pricing variant is genuinely 1.2 points worse, does that settle whether to stop it? What else is on the other side of that decision?
The trailing eight-week mean spans the processor migration. What is the right baseline instead?

Suggested practice checklist

Instead of guessing where the week should go, day one measures it under a fixed rubric and allocates the remaining hours in proportion to the gaps. The method is deliberately rigid: the allocation is written down before any studying starts and is not renegotiated when a topic turns out to be unpleasant.

Day 1 — Diagnostic, scored before you study anything

Sit a 100-minute timed diagnostic in four blocks: 30 minutes of SQL across three prompts, 25 minutes of short-answer statistics, 25 minutes on one modelling or case prompt, and 20 minutes delivering one behavioural story aloud.
Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct, and 0 is stuck, grading the output rather than how the attempt felt.
Allocate the hours for days two to five roughly in proportion to 3 minus the score in each block, write the allocation down, and commit to not revising it midweek.

Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.

Day 2 — Largest gap: find the boundary rather than the subject

Break the weakest area into five named sub-skills (for query work: grain control, window frames, date arithmetic, set logic with NULLs, and reading a query plan) and rate each one, so the rest of the week targets a sub-skill instead of a subject.
Solve three problems chosen to sit just above where the rating drops off, and for each write the first move you failed to make.
Re-solve one of them from memory four hours later, on paper, with nothing open.

Deliverable: A five-item sub-skill map with the two blocking sub-skills circled.

Day 3 — Largest gap: drill the blocking sub-skill

Do eight short repetitions of the same shape rather than eight different problems, so what you practise is the pattern and not the puzzle.
Write the rule you now hold in one sentence, then test it against a case built to break it: a ranking function over a column with ties, or a two-sample test on observations that are obviously dependent.
Have someone else read your one-sentence rule and find the precondition you left out.

Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.

Day 4 — Second gap, plus maintenance on your strongest area

Run the same sub-skill map and boundary protocol on the second-largest gap, compressed into half the day.
Spend 25 timed minutes on your strongest area to stop it decaying, choosing the hardest problem you can still finish rather than an easy warm-up.
Compare how the two areas fail: whether you lose time on recall, on setup, or on arithmetic, because the fix differs for each.

Deliverable: A second sub-skill map plus a one-line diagnosis of how each area fails you.

Day 5 — The gap that is not a skill

Record yourself answering one technical and one behavioural prompt, then count two things in the playback: how many seconds before your first clarifying question, and how many sentences you started without knowing where they ended.
Rewrite your three most-used stock phrases into shorter versions, and practise saying "I do not know, here is how I would find out" without softening it into a guess.
Deliver one answer again with a hard 90-second limit to force structure before detail.

Deliverable: Two recordings with a counted improvement in time-to-first-question.

Day 6 — Retest under day-one conditions

Sit the same 100-minute diagnostic structure with new prompts of comparable difficulty and score it on the identical rubric.
Compare block by block, and for any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
Write which single block you would still lose the offer on.

Deliverable: A second scored rubric placed next to the first, with one named remaining risk.

Day 7 — Full loop under interview conditions

Run a 60-minute mock covering the two blocks that moved least, with an interviewer instructed to interrupt and change direction.
Write your recovery script for the moment you go blank: restate the question, state your assumption, name the first thing you would check.
Reduce the week to the rule statements you wrote, each with its preconditions attached, then say every one of them out loud without reading it and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive being interrupted.

Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.

What the behavioural round is actually scoring

Interviewers here are not checking whether you can describe a project. They want the decision you made, why you made it under the information you had, and what changed afterwards that someone else could measure. A story that ends at 'I built a model' has no ending. Say what the model caused, or what you stopped doing because of it.

How do you handle missing data in a geospatial dataset?

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
State the situation in two sentences and spend the rest on your reasoning.
Close with what you would do differently, concretely.

Follow-up

How did you know the outcome was caused by your change?
What would you do differently if you ran that project again?

Quantify your own impact without claiming the topline you touched

Hard · self-assessment · attribution · communication

You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.

Approach

Recognise what is being probed: whether you apply to your own work the causal standard you would apply to somebody else's roadmap claim. Nearly everyone who would reject 'accounts that do Y retain better' will write 'I drove a 14% increase' without noticing it is the same error with a friendlier subject.
Sort the work by the kind of evidence it can carry. The shipped experiment is the only item with a randomised estimate, so it is the only one where an effect size is defensible, and you claim the interval rather than the point estimate.
Claim the three flat experiments as decisions prevented and price them. Features not built, or built differently, on evidence, with the engineering weeks reallocated as the number somebody else can verify. A defensible null is a delivered decision and should be written as one.
Claim the definition fix as correctness, not as improvement. The old figure was overstated by a specific percentage and appeared in a specific set of recurring documents; the impact is the change it produced in the forecast built on top of it, not a change in churn itself.
Claim the dashboard on usage and displacement: distinct weekly users of it, and the ad-hoc request count for six months before against six months after. If the request log does not exist, record the claim as unverified rather than estimating it upward.
Disclaim the 14% explicitly and once. State that it cannot be separated from seasonality, other teams' launches and a pricing change, and bound your own contribution from above using the shipped experiment's interval converted into headline units.

Follow-up

Your shipped experiment's interval was +0.2pp to +1.4pp on activation. How much of the 14% can that account for, and how do you say so without undercutting yourself?
A peer in the same cycle claims the full 14%. What, if anything, do you do about it?
If you could only keep two of your three claims, which do you drop, and why that one?

Defend a flat experiment readout against a post-hoc segment

Medium · experiments · segmentation · pushback

A feature you evaluated is flat on seven-day activation: +0.05pp with a 95% interval of [-0.47pp, +0.57pp], from 61,000 exposed users per arm in fct_experiment_exposure joined to dim_user and fct_event. Baseline activation is 32%. The launch team asks you to drop every surface except mobile_web, where the point estimate is +1.1pp, and re-run. You have ten minutes in their planning meeting. Deliver a spoken position: what you will and will not do, and the decision you recommend.

Approach

Recognise what is being probed: whether you hold a statistical position under social pressure without becoming either rigid or apologetic. A generic answer says the segment is not significant; a strong one separates the request into a question that is answerable (is the mobile_web number real?) and one that is not (can we ship on it?), and answers both.
Price the multiplicity out loud. The slice was chosen after seeing the results, so its estimate is selected on favourable noise and is biased away from zero. With k independent looks at a nominal 5% level, the chance of at least one false positive is 1 - 0.95^k: 26% at six segments, 64% at twenty. Quote the k you actually inspected, not the k you reported.
Use the arithmetic already in front of you. On the point estimates, a +1.1pp mobile_web effect combined with a pooled +0.05pp implies the remaining surfaces average negative in proportion to mobile_web's share of exposures. State that as a testable implication of their story rather than as a rebuttal of it.
Ask the one question that settles the category: was mobile_web named in the analysis plan before launch? If it was, it is a planned comparison and gets a corrected reading. If it was not, it is a hypothesis, and the honest move is to size the test that would confirm it.
Convert the refusal into a cost. Size a mobile_web-only confirmatory test at the claimed effect, state the weeks of mobile_web traffic it needs, and close with the recommendation: do not ship this as a lift, and note that the interval already rules out anything at or above +0.6pp, which is itself a useful input to the roadmap.

Follow-up

The confirmatory test you sized needs nine weeks of mobile_web traffic and the team has three. What do you recommend instead?
Suppose mobile_web was pre-registered. How does your reading change, and what correction do you apply?
Your interval excludes +0.6pp. Is that the same as saying the feature does nothing?