Interview guides · Fundbox

Fundbox Data Scientist Interview Questions & Guide 2026

Modeling drills for a Fundbox data scientist interview: baselines, regression interpretation, class imbalance, model defense.

The 60-second brief

At Fundbox, a Data Scientist is at the absolute core of the company's business model. As a financial technology platform dedicated to providing working capital and credit solutions to small businesses, Fundbox relies on sophisticated predictive models to assess creditworthiness, detect fraud, and automate underwriting processes in real-time. Without highly accurate, scalable machine learning models, the platform cannot safely or efficiently disburse funds to its customers.

Most of the loop measures decision-making under uncertainty rather than recall. You are scored on whether you state your assumptions, commit to an estimate you can defend, and say explicitly what evidence would change it.

Focus areas

Separate authorization, settlement and dispute outcomes cleanly
Decompose expected loss into PD, LGD, EAD
Reconcile amounts in minor units and currency

Overview

As a Data Scientist on this team, your work directly impacts the company’s bottom line and the financial health of thousands of small businesses. You will design, build, and deploy machine learning pipelines that analyze alternative data sources, transactional histories, and banking information. The models you build must balance risk mitigation with customer acquisition, solving complex credit-risk problems that traditional financial institutions struggle to address.

This role is highly collaborative and technically demanding. You will work closely with product managers, risk operations, and software engineers to translate raw financial data into actionable underwriting decisions. The fast-paced fintech environment requires you to build robust, production-grade systems while maintaining the flexibility to experiment with new algorithms and data sources.

The Fundbox interview process

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

HR Screen

Most candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.

What to demonstrate

Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
The substance of the questions you ask back, which an experienced screener reads as a level signal

How to prepare

Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered

Technical Screening

Much of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.

What to demonstrate

Whether the query you write matches the plan you just described
What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly

How to prepare

Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock

Home Assignment

Before any modelling, the dataset is itself the first test. Take-home data usually carries something broken: rows duplicated at an unexpected grain, a join that silently drops part of the population, timestamps stored in more than one timezone, or missingness correlated with the outcome. An hour spent profiling row counts, key uniqueness and date ranges is not overhead, because it decides whether every number after it is real. What separates submissions is whether you report the defects you found and adapt the analysis to them, rather than modelling over them quietly and hoping the aggregate absorbs it.

What to demonstrate

Whether you established the grain of each table and checked row counts after every join, and said so in the writeup
Whether data defects you found are surfaced with their effect on the conclusion, instead of being dropped without comment
Whether filters and exclusions are reproducible from the submitted code, with the size of the excluded population quantified

How to prepare

Write a short profiling script you can point at any unfamiliar table: row count, distinct key count, null rate per column, and the min and max of every date field, then run it before anything else
Write the funnel or the join chain as one query and check the row count at each grain, so a silent fan-out shows up as a number rather than as a wrong answer later
On a past dataset, list every exclusion you applied and how many rows each one removed, then draft the single sentence about it you would put in a report

Technical and Behavioral Interviews

Behavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.

What to demonstrate

Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier

How to prepare

For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.

Where candidates lose points

Using written premium as the denominator of a loss ratio

Premium is written at inception and earned pro rata across the exposure period, so in a growing book written premium runs ahead of earned premium and the loss ratio comes out too low, with the error reversing when the book shrinks. The numerator has the mirror-image problem if it omits incurred-but-not-reported reserves, since recent accident periods then look profitable twice over. Both sides must refer to the same exposure period, which is what an accident-period view at a fixed development age enforces.

Counting authorizations instead of weighting them, and summing amounts across currencies

Declines skew toward high-value, cross-border and card-not-present transactions, so an unweighted approval rate can sit flat while approved value falls. Merchant retry logic also turns one declined purchase into several rows, inflating the denominator by an amount that varies by merchant and by decline reason. Amounts are held in the minor unit of the transaction currency and that unit is not always two decimals, since some currencies have none and some have three, so summing amount_minor across currencies produces a figure with no interpretation at all.

Reporting a mean for a heavy-tailed metric without saying what it hides

For spend, session length or items per order, a small fraction of units carries most of the total, so the mean has a wide standard error and one account can move it. Fix the handling before you see the result: cap or winsorise at a pre-declared percentile, and report the median or the share above a threshold next to the mean. Capping changes the estimand, so say which question the capped number answers, and check how much of any difference comes from the top 0.1 percent of units.

Reading experiment results before checking the arm split

Compare observed arm counts against the intended allocation ratio, not an assumed even split, and set the alarm far below the conventional 0.05: at 0.05 roughly one healthy experiment in twenty trips it, which is why sample-ratio checks usually run at p < 0.001 or stricter. The test's power scales with sample size, so it misses a real diversion on a small experiment and fires on an imbalance too small to move the estimate on a very large one. A flag means go find the assignment or logging fault before reading any outcome, not report a mismatch.

Questions & practice

Describe how you would build a model to detect fraudulent credit applications in real-time.

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Frame the prediction: the label, the moment of prediction, and the action it triggers.
Say how the offline result would be validated online before it is trusted.

Follow-up

What would you monitor after launch to know the model is still valid?
How would you choose the decision threshold, and who owns that choice?

What are the trade-offs of using a random forest versus a logistic regression model in a highly regulated industry like fintech?

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Frame the prediction: the label, the moment of prediction, and the action it triggers.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

How would you choose the decision threshold, and who owns that choice?
What would you monitor after launch to know the model is still valid?

Explain the difference between bagging and boosting, and when you would choose one over the other for credit risk modeling.

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Pick an evaluation metric that matches the cost of each error type, not a default.
Set a baseline first, so any model has something honest to beat.

Follow-up

What would you monitor after launch to know the model is still valid?
How would you choose the decision threshold, and who owns that choice?

If you are given a dataset containing transactional banking history with missing values, how would you approach data cleaning and feature engineering?

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.
Say how the offline result would be validated online before it is trusted.

Follow-up

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

Build a vintage delinquency table without pivot or unstack

Easy · vintage analysis · cohorts · groupby · cumulative max

fct_loan_performance_monthly gives loan_id, origination_month, months_on_book, days_past_due, charge_off_flag and restructured_flag. Produce a DataFrame with one row per origination_month and columns for months_on_book 0 through 12, each cell holding the share of that vintage's funded loans that had ever reached 90 or more days past due, or charge-off, by that age. You may not use pivot, pivot_table, crosstab or unstack. Cells for ages a cohort has not yet reached must be NaN rather than zero.

Approach

Define the per-row indicator as days_past_due >= 90 or charge_off_flag, then take a cumulative maximum of it per loan ordered by months_on_book, because the metric is reached-by-age-m, not in-that-state-at-age-m.
Deal with restructuring before the cumulative max. Restructuring resets days_past_due, so a restructured loan re-enters at current and, without the cumulative maximum carrying its pre-restructure worst state, reads as a cure.
Fix the denominator once as the count of distinct loan_id per origination_month across the whole cohort. Prepaid and charged-off loans stop producing rows, so a denominator recomputed at each age silently shrinks exactly where losses land.
Aggregate with groupby(['origination_month','months_on_book'])['ever_90'].sum(), then pre-build the output frame indexed by sorted origination months with integer columns 0 to 12 and assign from the grouped Series by .loc on its index.
Mask cells beyond each cohort's maximum observed months_on_book so an immature cell reads NaN instead of an artificially low rate.

Worked solution (30 min)

Sort by loan_id and months_on_book, build the ever_90 indicator, then apply groupby('loan_id')['ever_90'].cummax().
Compute cohort_size as the distinct loan_id count per origination_month, before any filtering on age.
Group the cumulative indicator by origination_month and months_on_book and sum it to get the numerator per cell.
Create the output frame with the sorted origination months as index and range(0, 13) as columns, fill it from the grouped Series, and divide each row by its cohort_size.
Compute each cohort's maximum observed months_on_book and set every cell to the right of it to NaN.

Expected result: A DataFrame with one row per origination_month and columns 0 to 12, values non-decreasing left to right within every row, near zero in column 0, and NaN forming an upper-right triangle where cohorts have not yet aged.

Verify it yourself

Every row is non-decreasing across columns; a decrease means the cumulative maximum was not applied or a restructure reset leaked through.
The denominator per row equals the cohort's distinct loan count and does not change across columns.
Column 0 is at or near zero for every vintage, since a loan cannot be 90 days past due at its first month end.
origination_month plus months_on_book is constant along an anti-diagonal, so one anti-diagonal should reproduce a direct calendar-month filter.

Follow-up

Two adjacent vintages diverge at months_on_book 6. How would you separate seasoning, mix shift and a genuine credit-quality change?
The three most recent vintages look best on this table. What do you check before saying so?
How does the table change if charge-off policy moved from 180 to 120 days past due partway through the series?

Count-weighted and dollar-weighted approval rates on one currency

Easy · aggregation · denominators · minor units

Using fct_payment_authorization, report the trailing 7-day authorization approval rate two ways for transaction_currency = 'EUR': count-weighted, and dollar-weighted on amount_minor. Exclude is_reversal = true, exclude incremental authorizations (parent_auth_id not null), and exclude zero-amount account verifications. auth_result = 'approved' is the numerator; the four declined_* values make up the rest of the denominator. Return channel, attempts, approved_attempts, approval_rate_count and approval_rate_value. State every exclusion and its reason before you write the SELECT.

Approach

Say the denominator out loud first: attempts on a single transaction currency, excluding reversals, incremental authorizations and zero-amount verifications, because none of those is a purchase attempt a merchant is trying to get approved.
Filter requested_at against a half-open interval (>= start AND < end) so the boundary day is neither dropped nor double counted.
Compute both rates in one pass with FILTER clauses: COUNT() FILTER (WHERE auth_result = 'approved') over COUNT(), and SUM(amount_minor) FILTER (WHERE auth_result = 'approved') over SUM(amount_minor).
Cast one side of each ratio to numeric before dividing, since amount_minor and the counts are integers and integer division silently truncates to zero.
Group by channel and sort by the value-weighted rate, then read the gap between the two rates as a statement about where the declines sit rather than as noise.

Worked solution (20 min)

Write the exclusion list as comments above the query: is_reversal = false, parent_auth_id is null, amount_minor > 0, transaction_currency = 'EUR'.
Build a single aggregate query over fct_payment_authorization with a half-open requested_at predicate and those four filters.
Emit attempts, approved_attempts, approval_rate_count and approval_rate_value with FILTER clauses and a numeric cast on the numerator.
Group by channel, order by approval_rate_value ascending so the worst channel is on top.

Expected result: One row per channel present in the window, with attempts and approved_attempts as integers and both rates as numerics strictly between 0 and 1. approval_rate_value is typically lower than approval_rate_count on ecommerce and moto, because declines concentrate in higher-value card-not-present traffic.

Verify it yourself

SUM(attempts) across channels equals a separate COUNT(*) run with the same four filters and no GROUP BY.
Dropping the amount_minor > 0 filter raises attempts and moves approval_rate_count while leaving approval_rate_value unchanged, which confirms the zero-amount rows were only ever affecting the count-weighted figure.
Both rates are strictly less than 1 unless a channel had no declines at all in the window.

Follow-up

The two rates diverge by four points on the ecommerce channel but agree on card_present. What does that tell you, and what would you cut next?
How would you extend this to all currencies without summing amount_minor across them?
Which of the four decline reasons belong in the denominator of a rate you would put in front of a risk team, and which are really the network's problem?

Collapse merchant retry chains before measuring authorization approval

Medium · window functions · gaps and islands · sessionisation

Merchant retry logic turns one declined purchase into several rows in fct_payment_authorization. Collapse attempts into chains: inside a partition of (card_token_id, merchant_id, amount_minor), consecutive rows whose requested_at gap is 15 minutes or less belong to the same chain. Return one row per chain with chain_started_at, attempt_count, amount_minor, channel, and chain_outcome set to 'approved' when any attempt in the chain was approved. Then report a 7-day rolling dollar-weighted approval rate by channel over chains rather than attempts. Exclude is_reversal rows.

Approach

In a CTE, take LAG(requested_at) OVER (PARTITION BY card_token_id, merchant_id, amount_minor ORDER BY requested_at) and set is_new_chain when the previous value is null or the gap exceeds 15 minutes.
Turn the flag into a chain identifier with SUM(is_new_chain::int) OVER (same partition, same order, rows unbounded preceding to current row), which is the standard gaps-and-islands construction.
Aggregate to one row per chain: MIN(requested_at), COUNT(*), and BOOL_OR(auth_result = 'approved') as the chain outcome, because the purchase succeeded if any attempt in the chain did.
Roll the chains to a daily grain per channel, then apply the rolling window as PARTITION BY channel ORDER BY chain_date RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW. An interval-offset RANGE frame is value-based: it admits every row in the partition whose chain_date falls in the closed window [D - 6 days, D], so a channel with no chains on some dates still gets the right seven-day sums — sparsity is exactly what this construct tolerates. ROWS BETWEEN 6 PRECEDING AND CURRENT ROW is the one that needs a dense grain, because it counts rows and on a sparse grain reaches back further than seven days.
Left-join a dense date spine only if the output needs a row on days with no chains. That is a presentation requirement, not a correctness one, and it leaves every rolling sum unchanged. The RANGE frame's preconditions are that chain_date is a date or timestamp (an interval offset needs an ordering column it can be added to) and that the aggregation left one row per (channel, chain_date).
Divide approved chain value by total chain value inside the rolling frame, never the average of daily rates, since averaging rates weights a quiet day the same as a heavy one.

Follow-up

This definition chains off the previous attempt, so a chain can span an hour if attempts arrive every 14 minutes. When is that right, and when would you anchor the window to the first attempt instead?
Two genuine purchases of the same amount at the same merchant, four minutes apart, collapse into one chain. How large is that error and how would you bound it?
A channel goes dark for three days mid-window. Which of the two frame types would silently widen, and what would the resulting rate look like?
What changes if the merchant retries with a slightly different amount to dodge a velocity rule?

Price a false decline when the label does not exist

Hard · proxy metrics · selection bias · randomised holdout

Your expected-cost threshold needs a figure for what a false decline costs beyond the margin on the blocked transaction. fct_payment_authorization records the decline, nothing records what the customer did next elsewhere, and declined transactions never produce a fraud outcome, so neither side of the error is directly observable. Propose the measurement: the proxy you would build from the tables you have, the design that yields an unbiased estimate for at least part of the score range, the bias in each, and the single sentence you would attach to the number when it reaches a pricing decision.

Approach

Write down what is unobservable and why. The counterfactual spend of a customer who was not declined, and the fraud label on any transaction the rule blocked, are both missing because of the decision itself. Missingness that depends on the decision is not fixed by matching on observed covariates.
Build the observational proxy anyway and be specific. For customers receiving a first risk-rule decline in a window, compare settled volume and active status over the following 30 and 90 days against customers matched on pre-period settled volume, tenure, segment and channel mix who attempted a comparable transaction and were approved. The bias runs one way: matching conditions on having attempted something that scored near the cutoff, and part of the declined group are genuine fraudsters whose disappearance is a saving rather than a loss, so the estimate overstates the damage.
Buy one unbiased local estimate. Hold a small random share of authorizations inside a defined risk_score band out of the decline rule and approve them, sizing the sample in advance from the expected fraud rate in that band so the cost of the experiment is known before it runs. The result is unbiased for that band only, and it is simultaneously the only source of fraud labels in the declined region.
Bound the extrapolation instead of hiding it. Run the holdout in two or three adjacent bands and report the spread. If the effect is flat across bands a constant is defensible; if it is steep, quote band-specific figures and decline to supply a single number.
Handle the window. Attrition after a decline can resolve over months, so a 90-day window truncates it and the randomised estimate is a lower bound on long-run damage at the same time as the observational version is an upper bound. Stating both directions is what makes the number safe to use.
Write the sentence that travels with the number: what it is (an estimate from a randomised holdout in one score band over a 90-day window), what it is not (a measurement anywhere else on the score range), and which way it is likely to be wrong.

Worked solution (40 min)

Define the decline cohort and the matched comparison cohort precisely, including matching variables and the pre-period window, and produce the 30-day and 90-day settled-volume difference.
Decompose the declined cohort into customers who never transact again and customers who transact less, since fraudsters concentrate in the first group and that split tells you how much of the estimate is contamination.
Write the holdout design: score band, sample share, expected fraud rate in band, expected cost of running it, and the run length needed to detect an effect large enough to change the cutoff.
Recompute p* = C_FP / (C_FP + C_FN) at the top and bottom of your estimated cost range and state whether the range changes the cutoff you would set.
Write the one-sentence caveat that will be quoted alongside the number in the pricing decision.

Expected result: Two numbers with opposite biases, an observational estimate labelled as an upper bound and a randomised in-band estimate labelled unbiased for that band only, plus a sensitivity table showing how p* moves across the range and an explicit statement of whether the residual uncertainty changes the decision.

Verify it yourself

If the cost range moves p* by less than the granularity at which the rule can actually be set, further measurement is not worth buying and that is the finding.
The observational estimate should exceed the randomised one in magnitude. If it does not, the matching is capturing something other than the effect of the decline and needs re-examining.
Removing customers with zero prior settled volume from both cohorts should shrink the observational estimate, since new-account fraud concentrates there.

Follow-up

Compliance and finance both object to deliberately approving transactions you believe are fraudulent. What is your answer, and how do you size the holdout?
Your interval spans the decision boundary. What do you recommend?
How would you detect that this number has gone stale?

A dispute rate that does not fall at the right edge

Medium · right-censoring · development factors · cohort maturity

Your chargeback chart slopes downward over the last four months every time it is refreshed. It is built from fct_card_dispute counted by opened_at, divided by settled transactions in the same month. Using auth_id, requested_at, dispute_stage, opened_at, network_reason_code and outcome, explain what the chart is actually showing, then specify the metric you would publish: attribution, denominator, maturity rule, and how immature periods appear on the chart. State the condition under which you would publish a development-factor estimate instead of a blank.

Approach

Separate the two dates. A dispute belongs to the month of the transaction that caused it, which is requested_at on the linked authorization, not to the month the case was filed. Counting by opened_at mixes old transactions into new months and makes the series unreadable in both directions at once.
Once attribution is fixed, name the censoring. Recent transaction months have not had time to accumulate their disputes, so the right edge falls by construction. The slope will still be there next month, one month further right, which is the diagnostic that distinguishes it from a real improvement.
Set the maturity threshold empirically rather than by folklore. From completed transaction months, compute the share of the final dispute count filed by 30, 60, 90, 120 and 180 days, split by reason-code family. Consumer dispute rights commonly run around 120 days from the transaction or the expected delivery date, but several reason codes run materially longer, so one global cut-off under-covers the slow families.
Specify the published metric: disputes reaching dispute_stage 'first_chargeback' or later, attributed to the transaction month, over settled transactions in that same month, with only months past the measured threshold shown as final.
Decide how immature months appear. Either omit them, or show a chain-ladder style estimate with an interval in visibly different styling, never as a plain point on the same line as final months. Publish the estimate only when the development pattern is stable across recent completed cohorts; if the reason-code mix or the dispute process has shifted, the factors are estimated on a pattern that no longer applies.
Note that the count series and the value series mature at different speeds, since net_loss_minor is signed and known only after the case resolves, so a loss-value chart is stale for longer than a case-count chart of the same data.

Follow-up

Your development factors were estimated over a period containing an authentication policy change. What do you do?
How do you present a month that is 60 days mature to an audience that wants a single number?
Would you attribute a dispute to the authorization month or the settlement month, and when does the difference matter?

Fraud losses appear to halve in recent transaction months

Medium · right censoring · development factors · maturity

A weekly chart attributes fct_card_dispute cases to the requested_at month of the linked fct_payment_authorization row. The two most recent months show the first-chargeback rate falling by half, and a risk rule shipped six weeks ago. Columns: dispute_id, auth_id, dispute_category, dispute_stage, opened_at, disputed_amount_minor, liability_shift_flag, outcome, net_loss_minor, resolved_at. Decide whether the rule worked, and produce the version of the chart you would sign your name to.

Approach

Separate the two dates explicitly. opened_at is when a case was filed, requested_at is when the transaction happened. Attributing by transaction month is the right causal choice and is exactly what makes the newest months structurally incomplete.
Measure the filing lag rather than assuming it: the distribution of opened_at minus requested_at over fully developed months, split by dispute_category, and the age at which around 95 percent of cases have arrived.
Build a development triangle of transaction month by months of development on cumulative case counts, and estimate age-to-age factors from the columns that are complete.
Develop the immature months with those factors and plot the result as an estimate with a visible band, kept visually distinct from the matured series rather than blended into it.
State the assumption the method needs: a stable development pattern across cohorts. A change in filing behaviour, merchant mix or the dispute team's own backlog breaks it, so inspect factor stability down each column before relying on the estimate.
Only then evaluate the rule, comparing pre-change and post-change cohorts at equal development age.

Follow-up

What leading indicator would you accept while the cohort matures, and what is its known bias?
How does liability_shift_flag change which disputes you should expect to see in the first place?
If the rule also blocked good transactions, where does that cost appear, and is any of it in this chart?

Suggested practice checklist

For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.

Day 1 — Design one test end to end on paper

Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
State in advance what you will do if the primary metric is flat while a secondary metric is significant.

Deliverable: A one-page test design with a decision rule written before launch.

Day 2 — Power arithmetic until it is automatic

Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.

Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.

Day 3 — Variance and the unit-of-analysis problem

Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.

Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.

Day 4 — Validity threats you can actually test for

Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.

Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.

Day 5 — When randomization is not available

Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.

Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.

Day 6 — The readout query

Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.

Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.

Day 7 — Present it to someone who will not read the appendix

Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
Rewrite your opening line so the recommendation lands before any methodology.

Deliverable: A one-page readout whose first line is the recommendation.

Stories where someone disagreed with you

Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.

Tell me about a project where you realized your initial modeling approach was not working. How did you pivot?

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
Pick a story where you drove the decision, not one where you observed it.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

What would you do differently if you ran that project again?
What did you decide not to do, and why?

Describe a time when you had to explain a complex technical model to a non-technical stakeholder. How did you structure your explanation?

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
Pick a story where you drove the decision, not one where you observed it.
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?

Allocate one analyst-week across three competing risk requests

Medium · prioritisation · decision deadlines · stakeholder negotiation

Three requests land in the same week and you have one analyst-week. Payments wants a merchant-level decline teardown before a contract renewal in nine days. Credit wants a swap-set analysis on a cutoff change scheduled to ship in six weeks. Insurance wants accident-quarter loss ratios at 12 months development for a reserving review with no fixed date. Each sponsor believes theirs is first, and each has escalated before. Produce the allocation, the reasoning you would say out loud to all three at once, and what you explicitly drop.

Approach

Score each request on the decision it unblocks rather than on effort or on how loudly it arrived: what changes if it is late, and is that change reversible.
Separate deadline from value. The nine-day renewal is a hard, irreversible date with a bounded prize; the six-week cutoff has slack but a much larger downside if it ships unmeasured; the reserving number has no date but feeds external reporting, which is its own kind of hard.
Hunt for the cheap partial in each: a decline teardown restricted to the top merchants by declined value usually answers the contract question at a fraction of the full cut.
Sequence by hard date first, then by largest irreversible downside, and deliver the trade-off to all three sponsors in one message rather than three, so nobody negotiates privately against a version you told someone else.
Name what is dropped and who now owns that consequence, in writing, so the trade-off is visible rather than silently absorbed by you.

Follow-up

The credit sponsor escalates to your manager. What do you change, and what do you refuse to change?
How would you make this allocation reproducible so the next contested week is a rule application rather than a negotiation?