A Data Scientist at Expleo Group plays a crucial role in transforming data into actionable insights that drive business decisions and enhance product development. This position is integral to the company’s mission of delivering innovative solutions across various sectors, including automotive, aerospace, and technology. By analyzing complex datasets, you will help in understanding customer behaviors, optimizing processes, and contributing to the strategic growth of the organization.
In your role, you will collaborate closely with cross-functional teams, including engineering, product management, and operations. This collaboration allows you to tackle sophisticated problems using advanced analytical techniques, machine learning algorithms, and statistical methods. The impact of your work will be significant; your contributions will not only influence product design and functionality but also improve user experiences and operational efficiencies.
Working as a Data Scientist at Expleo Group means engaging with diverse datasets and employing cutting-edge tools and technologies. You will have the opportunity to work on projects that are both challenging and rewarding, making your role vital to the overall success and innovation of the company.
Phone Screen
reportedA screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.
What to demonstrate
- Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
- Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
- Whether timeline, location and compensation expectations make the rest of the loop worth scheduling
How to prepare
- Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
- Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
- Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice
Technical Interview
reportedMuch 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
Behavioral Assessment
reportedThis round decides whether you owned a decision or watched one happen nearby. Interviewers for data roles listen for the point where the analysis stopped being a report and started changing what someone did, so build each story around that hinge: what was going to happen by default, what you found, and what happened instead. The most common weakness is a story that ends at delivery. If you can name the decision your work changed and the number that moved because of it, most follow-ups become easy.
What to demonstrate
- Whether the decision was yours to influence, or whether you are narrating a team outcome in the first person
- The counterfactual: what would have been done without your analysis, and why that default was worse
- How far your involvement ran past the handoff, and whether you checked that the change did what you predicted
How to prepare
- Pick three projects and write one sentence for each naming the decision-maker, the choice in front of them, and what they chose after seeing your work. If you cannot name a person and a choice, the story is not ready for this round.
- Reconstruct the baseline for your strongest project from the original query or dashboard rather than memory, so the before-number survives a follow-up asking where it came from.
- Prepare an honest version of a project where your recommendation was overruled, including what you did with the analysis afterwards.
PracHub editorial advice for the preparation topics above.
Randomising an experiment at the user level when users share an account
Two problems fire at once. Colleagues in one workspace see each other's work and talk to each other, so a treated user changes the behaviour of a control user in the same account, which violates the no-interference assumption and biases the estimate toward zero. Separately, outcomes within an account are strongly correlated, so the effective sample size is roughly n / (1 + (m - 1) * rho) for m users per account and intra-class correlation rho, not n. With rho around 0.3 and twenty users per account that is a design effect near 6.7, meaning a user-level confidence interval is about two and a half times narrower than it should be and results cross significance thresholds on noise alone. Randomise the account and cluster the standard errors.
Treating raw request or usage volume as engagement
Most traffic in this domain is emitted by machines. Continuous-integration pipelines, scheduled batch jobs, synthetic monitors, backfills and client retries can all grow by an order of magnitude from one configuration change made by one engineer, and none of it represents a new decision to use the product. The inversion is what makes it dangerous: when the platform degrades, clients retry, so error-driven retry volume rises at the exact moment the customer is most likely to leave, and an engagement dashboard built on raw counts shows growth immediately before a churn. Filter on traffic_class and on successful status before anything else, and keep failed-request volume as its own separate series.
Averaging per-user rates to produce a population rate
Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.
Over-explaining the method and under-explaining the implication
Lead with the answer and what you would do about it, then give the approach when asked. Roughly one sentence of method per three of implication is the right ratio for a stakeholder-facing answer; the interviewer already knows what a regression is.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function to calculate the mean and standard deviation of an ar…
Write a function to calculate the mean and standard deviation of an array of numbers.
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Write down the assumption the method needs before you use the method.
- Sanity-check the answer against a simple bound or a simulated case.
Follow-up
- What sample size would you need to detect an effect half this size?
- Which assumption here is most likely to be violated in practice?
What are some common evaluation metrics for classification problems?
What are some common evaluation metrics for classification problems?
Approach
- Check what information would not exist at prediction time, and exclude it.
- Say how the offline result would be validated online before it is trusted.
- 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?
- Where could label leakage enter this setup?
Explain the concept of a decision tree and how it works.
Explain the concept of a decision tree and how it works.
Approach
- Check what information would not exist at prediction time, and exclude it.
- 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.
Follow-up
- What would you monitor after launch to know the model is still valid?
- Where could label leakage enter this setup?
Join daily usage to the contract version live that day
fct_usage_daily has account_id, usage_date and net_amount_cents. fct_subscription_period has account_id, subscription_period_id, plan_tier, term_start_date, term_end_date, arr_cents and is_current, with one row per contract term version and every amendment inserting a new row. Attach to each usage row the subscription_period_id whose term brackets usage_date (term_start_date <= usage_date <= term_end_date). pd.merge_asof and an interval-condition merge are unavailable; use sorting and numpy.searchsorted. Then report monthly net revenue by plan_tier. Terms for one account do not overlap, and usage may fall outside every term.
Approach
- Say out loud why the cheap version is wrong: joining on is_current stamps today's plan tier onto last year's usage, so every account that upgraded has its history reclassified and revenue-by-tier becomes a function of when the query ran.
- Sort each account's terms by term_start_date and use np.searchsorted(term_start, usage_date, side='right') - 1 to get the last term that started on or before the usage date. Do it per account group, or globally after encoding (account_id, date) into one monotone key.
- searchsorted only enforces the left edge. Validate the right edge afterwards — usage_date <= the candidate's term_end_date — and set the match to NA where it fails. That NA is usage in a gap between contracts and must stay visible instead of being folded into the expired term.
- Assert non-overlap before trusting the lookup, and write the assertion so it is capable of passing. prev_end = terms.groupby('account_id').term_end_date.shift() is NaT on each account's first row, and NaT < Timestamp evaluates to False rather than NA, so a comparison followed by .fillna(True) has nothing left to fill and the assertion fires on every account's first term whatever the data looks like. Guard the null yourself: assert (prev_end.isna() | (prev_end < terms.term_start_date)).all(). The failure mode of getting this wrong is not a false alarm you notice once — it is an assertion someone deletes because it never passes, after which overlapping terms make searchsorted return one of them with no trace in the output.
- Aggregate after the join, grouping by (usage_date month, plan_tier) with dropna=False so the unmatched bucket appears as its own row and the total still ties to the ungrouped sum of net_amount_cents.
Worked solution 35 min
- terms = terms.sort_values(['account_id','term_start_date']); prev_end = terms.groupby('account_id').term_end_date.shift(); assert (prev_end.isna() | (prev_end < terms.term_start_date)).all()
- Per account group: idx = np.searchsorted(g.term_start_date.values, u.usage_date.values, side='right') - 1; rows with idx < 0 are unmatched.
- Gather subscription_period_id, plan_tier and term_end_date by positional index, then null the match wherever usage_date > the gathered term_end_date.
- monthly = joined.assign(month=joined.usage_date.dt.to_period('M')).groupby(['month','plan_tier'], dropna=False).net_amount_cents.sum()
Follow-up
- An amendment takes effect on the 17th of a month. How do you report that month's revenue by tier?
- What changes if terms can overlap because of a co-term amendment?
- How would you verify this against a SQL implementation using a BETWEEN condition?
How do you optimize a SQL query for better performance?
How do you optimize a SQL query for better performance?
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- State the window function and its partition and ordering out loud before writing it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Running commitment burn-down and the date consumption crosses it
fct_subscription_period gives committed_amount_cents, term_start_date and term_end_date for each account's current version where pricing_model = 'committed_consumption'. fct_usage_daily gives account_id, workspace_id, sku_code, usage_date and net_amount_cents, at one row per workspace and SKU per day. Inside each account's current term, return the running total of net_amount_cents by usage_date, the first usage_date on which that running total reaches committed_amount_cents, and the fraction of the term elapsed at that point. Accounts that have not reached their commitment must still appear, with a null crossing date.
Approach
- Collapse usage to one row per (account_id, usage_date) first. The fact is grained by workspace and SKU, so a raw running total leaves several rows per date and the first-crossing date becomes dependent on the arbitrary order of rows inside that day.
- Restrict to the term with usage_date BETWEEN term_start_date AND term_end_date on the account's current row, so no prior term's consumption leaks into this term's burn-down.
- Compute SUM(net_cents) OVER (PARTITION BY account_id ORDER BY usage_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW). Name ROWS explicitly: the default frame is RANGE, which includes all peer rows at the same ORDER BY value and would hand back the whole day's total on each of that day's rows.
- Extract the crossing with MIN(usage_date) FILTER (WHERE running_cents >= committed_amount_cents) grouped per account, which returns NULL for accounts still under commitment instead of dropping them.
- Elapsed fraction is (crossing_date - term_start_date)::numeric / NULLIF(term_end_date - term_start_date, 0); Postgres date subtraction yields whole days, so the guard matters for same-day terms.
- Exclude the trailing days still inside the metering settling window, measured from first_written_at against restated_at, and say how many days you cut and why.
Worked solution 30 min
- Build terms: SELECT account_id, committed_amount_cents, term_start_date, term_end_date FROM fct_subscription_period WHERE is_current AND pricing_model = 'committed_consumption' AND committed_amount_cents IS NOT NULL.
- Build daily: join fct_usage_daily to terms on account_id with usage_date inside the term, then GROUP BY account_id, usage_date summing net_amount_cents.
- Add running_cents with SUM(...) OVER (PARTITION BY account_id ORDER BY usage_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW).
- Aggregate per account with MIN(usage_date) FILTER (WHERE running_cents >= committed_amount_cents) AS crossing_date, then compute the elapsed fraction with the NULLIF guard.
- LEFT JOIN that summary back onto terms so every committed account appears, and verify the last running_cents per account equals a plain SUM over the term.
Follow-up
- Turn this into an end-of-term overage forecast. What breaks if you extrapolate a linear run rate on a consumption product?
- An account crosses its commitment at 40 percent of the term. Is that an expansion signal or a billing-surprise risk, and what would you check to tell them apart?
Walk us through your thought process for a recent data analysis projec…
Walk us through your thought process for a recent data analysis project.
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.
- State what result would change your recommendation, so the answer is falsifiable.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
How do you prioritize tasks when managing multiple projects?
How do you prioritize tasks when managing multiple projects?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- Restate the decision this analysis has to support, and who acts on the answer.
- Fix the population and the time window before naming any metric.
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, how would you approach the analysis to derive insight…
Given a dataset, how would you approach the analysis to derive insights?
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?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you design an experiment to test the effectiveness of a new …
How would you design an experiment to test the effectiveness of a new product feature?
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- Name the randomisation unit first; it decides the variance and what the test can detect.
- State the primary metric and the minimum effect worth shipping, then size the test.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- What would you do if you could not randomise at all?
What is the difference between supervised and unsupervised learning?
What is the difference between supervised and unsupervised learning?
Approach
- Say what you would check first and why it is the highest-information step.
- Clarify what is being asked and what a complete answer would contain.
- Work from the decision backwards to the evidence you would need.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Set a stopping rule for a daily-read experiment dashboard
An experiment dashboard recomputes a two-sided t-test every morning for 21 days across one primary metric and eleven guardrails. The team ships whenever anything crosses p < 0.05 and stops the test that day. Quantify the false-positive rate this procedure actually carries, from repeated looks and from the twelve metrics separately, then specify the readout protocol you would put in its place: what is checked daily, what is checked at pre-declared points, what controls the family-wise or false-discovery rate, and what the stopping rule is.
Approach
- Separate the two inflations, since they compound and have different remedies. Repeated fixed-horizon testing on accumulating data: at roughly 20 equally spaced looks with nominal alpha 0.05, the probability of crossing at least once under the null is about 0.25, and about 0.19 at 10 looks (Armitage, McPherson and Rowe, 1969). Multiplicity: twelve independent metrics at 0.05 give 1 - 0.95^12 = 0.46 of at least one false positive, somewhat lower under correlation but not much lower when metrics share a denominator.
- Point out that the stopping rule is worse than either figure alone. Stopping the moment anything crosses is optional stopping on the maximum over twelve metrics and twenty-one days, so the two inflations multiply rather than average, and the realised error rate is above 0.5 for a null effect.
- Rebuild around one pre-registered primary metric that alone decides ship or no-ship. Secondary metrics are demoted to hypothesis generation, reported with intervals and no significance marks, and are not permitted to trigger a ship on their own under any circumstances.
- For the primary, choose deliberately between two valid options and say what each costs. An alpha-spending boundary with a small number of pre-declared interim analyses, where an O'Brien-Fleming shape spends very little alpha early so the final look retains nearly the full amount; or an always-valid confidence sequence, which permits unlimited looks at the price of a wider interval at every fixed time, typically requiring on the order of 1.2 to 1.5 times the sample to match the fixed-horizon width. Whichever is chosen goes into the power calculation before launch, not after.
- Control the guardrail family separately from the primary. Benjamini-Hochberg at q = 0.10 when a missed regression costs more than an occasional false alarm; Holm when any single false positive triggers an expensive rollback. Say which regime you are in rather than reaching for a correction by habit.
- Distinguish diagnostics from readouts and write both lists down. Sample ratio mismatch, exposure counts, error rates and pipeline health are checked every single day and are not peeking, because their only permitted action is to abort the test, never to declare a win. The abort conditions belong next to the stopping rule in the same document.
Worked solution 25 min
- Record the repeated-testing inflation for 21 daily looks against the nominal 0.05, about 0.25 at 20 looks, either from published tables or by simulating a random walk under the null.
- Compute the independence bound 1 - 0.95^12 = 0.46, then re-estimate by simulation using the observed correlation matrix of the twelve metrics.
- Pick the sequential method and size the test for it, adding the confidence-sequence width penalty to the power calculation or placing two interim looks under an O'Brien-Fleming boundary.
- Write the one-page protocol: primary metric, stopping rule, interim dates, guardrail family and its FDR level, abort conditions, and the list of daily diagnostics that explicitly cannot trigger a ship.
Follow-up
- Your confidence sequence never crosses in 21 days, but the point estimate is exactly the size you powered for. What do you tell the team, and what do you do next?
- The eleven guardrails are heavily correlated with each other. Does that make Benjamini-Hochberg conservative or anti-conservative here, and does it change your q?
- Someone asks for a directional read on day five. What can you honestly give them without compromising the stopping rule?
Weekly active organisations fell nine percent over one week
A dashboard reports the weekly active organisation ratio on a trailing seven-day window ending each Wednesday. This week it reads nine percent below last week. You have fct_api_request (account_id, environment, traffic_class, http_status, request_at) and dim_account (account_id, billing_country, account_status, is_internal, is_current). Nothing was released. Decide whether usage actually fell, and hand back a corrected series plus a one-paragraph explanation that a non-analyst can repeat without you in the room.
Approach
- Count the holiday-free business days inside each window before comparing them. A trailing seven-day window spans exactly five weekdays wherever it ends, so its business-day count can only fall to four or fewer when a public holiday lands inside it and can never reach six, while usage in this domain follows a hard five-to-two weekday cycle. Two windows holding different numbers of business days are not comparable whatever the product did.
- Count distinct active accounts per calendar day for the last ten weeks and overlay the two windows. A calendar problem shows as a small number of weekdays sitting at weekend level, not as every day being uniformly lower.
- Cut the daily series by billing_country and index each country-day to that country's trailing same-weekday median, which isolates a regional public holiday from a product change.
- Check the denominator on its own: the metric divides by accounts whose account_status was in trial, free or active_paid for the whole week, so a batch suspension or status backfill moves the ratio with no change in the numerator at all.
- Report the series with each window's business-day count and the holiday dates annotated beside it, and state the residual week-over-week change that survives once the calendar effect is removed. Compare against earlier windows holding the same number of business days rather than dividing by business days, since distinct-account counts are sublinear in window length and dividing would over-correct.
Follow-up
- Distinct account counts are sublinear in the number of days in the window. Why does losing one of five business days reduce the count by noticeably less than twenty percent?
- How would you make this metric comparable across countries with different holiday calendars without hand-maintaining a holiday table forever?
Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Breadth pass: query fluency
- Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
- For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
- Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.
Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Breadth pass: statistics and inference
- Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
- Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
- Rewrite the two weakest answers the following morning from memory in full sentences.
Deliverable: Ten graded answers with an honest count of exact hits.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Breadth pass: modelling
- Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
- Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
- Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.
Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.
Practice prompt ↗Practice prompt ↗04Breadth pass: product judgement
- Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
- For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
- Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.
Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Depth, first area
- Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
- Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
- Re-solve the two you failed the same evening with notes closed.
Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.
Practice prompt ↗Practice prompt ↗06Depth, second area, and the seam between them
- Repeat the depth protocol on the second-ranked area with the same six-problem structure.
- Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
- Solve your own combined problem end to end and note where the handoff between the two areas cost you time.
Deliverable: One combined problem, solved end to end, with the handoff failure written down.
Practice prompt ↗Practice prompt ↗07Integration and re-measurement
- Re-run the six prompts from day one under the same clock and compare both correctness and time.
- Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
- Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.
Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
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.
How do you approach feedback and continuous improvement?
How do you approach feedback and continuous improvement?
Approach
- Pick a story where you drove the decision, not one where you observed it.
- Name the disagreement or constraint, and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on your reasoning.
Follow-up
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
Announce a metric fix that cuts the headline number
Weekly active organisations, the count on the company dashboard, has never excluded rows where dim_account.is_internal is true, and it counts traffic with traffic_class in synthetic_monitor and load_test. Correcting both reduces that count by 11 percent and removes most of the growth reported over two quarters. The figure appears in a board deck and in two teams' quarterly goals, one written on the count and one on the weekly active organisation ratio, whose denominator is accounts whose account_status was in ('trial','free','active_paid') through the week. Decide the order in which you tell people, what the dashboard shows during the transition, and what you propose happens to goals already set against the old definition.
Approach
- The interviewer is probing whether you can land a correction as an operational change with a plan attached, rather than as an announcement other people then have to clean up after.
- Quantify each exclusion separately before telling anyone: internal accounts, synthetic monitors, load tests. Three known quantities are a discussion; one alarming total is an argument.
- Be precise about which side of the metric each exclusion touches, because one team's goal is on a count and the other's is on a ratio. The traffic-class filters remove requests, so they shrink the numerator only. Dropping internal accounts removes them from the ratio's denominator as well, since internal accounts carry ordinary account_status values and therefore sit in that denominator. Internal accounts are active in almost every week while the real base is not, so the numerator loses a larger share than the denominator and the ratio falls by less than the count does. Compute both and say which one the 11 percent is before anybody assumes.
- Check whether the trend changes, not only the level. A constant 11 percent shift is a rebasing and nothing more. A shift that widens over time means the reported growth was partly internal or synthetic, which makes the existing goals unachievable as written and changes what you are asking teams to do.
- Sequence the disclosure: the metric owner and the two teams whose goals move first and privately, then the board channel with a written bridge, then the dashboard. The dashboard is last because a number that changes without explanation is read as instability rather than as a fix.
- Run both series for one reporting period with the bridge visible, restate history rather than letting the series break at a date, and set the date the old series is removed.
- Propose the goal treatment yourself: rebase each target by the shift measured on the metric that target is written against, rather than leaving each team to negotiate individually, which is where corrections of this kind usually die.
Follow-up
- One team's quarterly goal is now unreachable. Rebase the target or let it miss, and what does each choice teach the organisation?
- How would this have been caught when the metric was first defined?
- What else on that dashboard shares this failure mode, and how would you find out this week?
Scope an open-ended request to predict account churn
A customer success director asks for a list of accounts about to churn. You know only that the team has six people and that contracts are annual. Available data is fct_subscription_period, fct_usage_daily, fct_api_request, fct_support_ticket and dim_account. Before writing any code, produce the questions you need answered, a proposed definition of about to churn, and the shape of the artefact you would hand back, including the operating point that turns a score into a decision.
Approach
- The interviewer is probing whether you convert a vague request into a decision with a capacity constraint attached. A candidate who starts talking about model families has already failed the exercise.
- Pin the event and the horizon first. Churn is only possible at term_end_date, so the population is accounts renewing in the next 60 to 90 days, not the whole base. Ask explicitly whether contraction and downgrade count as churn or only full non-renewal, because the three have different base rates and different interventions.
- Pin the action and the capacity. Six people times a realistic number of meaningful interventions per week gives k, and k is what the list is ranked to. Evaluate on precision at k rather than a global AUC over accounts that will never be contacted.
- Audit leakage before choosing features. Every feature needs a timestamp proving it existed before the prediction date. A downgrade amendment, a churn reason code, and a ticket opened after the renewal conversation started are all leaks that will make the offline number look excellent and the live list useless.
- Ask for the counterfactual now rather than later. Coverage is assigned deliberately, so without a held-out slice agreed at the start the intervention can never be evaluated, and you will be asked for its impact in nine months regardless.
- Propose the smallest artefact that closes the loop: a weekly ranked list sized to capacity with two or three inspectable reasons per row, plus a stated policy for accounts below the line.
Follow-up
- The director insists all accounts are in scope, not only those renewing soon. How do you answer without simply refusing?
- Historical non-renewals number about 30 a year. At what point do you tell them a model is the wrong tool and a rules list is better?
- Which candidate features would you drop purely because you cannot date them?
- 01
How do you approach feedback and continuous improvement?
- 02
Weekly active organisations, the count on the company dashboard, has never excluded rows where dim_account.is_internal is true, and it counts traffic with traffic_class in synthetic_monitor and load_test. Correcting both reduces that count by 11 percent and removes most of the growth reported over two quarters. The figure appears in a board deck and in two teams' quarterly goals, one written on the count and one on the weekly active organisation ratio, whose denominator is accounts whose account_status was in ('trial','free','active_paid') through the week. Decide the order in which you tell people, what the dashboard shows during the transition, and what you propose happens to goals already set against the old definition.
- 03
A customer success director asks for a list of accounts about to churn. You know only that the team has six people and that contracts are annual. Available data is fct_subscription_period, fct_usage_daily, fct_api_request, fct_support_ticket and dim_account. Before writing any code, produce the questions you need answered, a proposed definition of about to churn, and the shape of the artefact you would hand back, including the operating point that turns a score into a decision.
Is this an official Expleo Group interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Expleo Group. Rounds and questions reflect what candidates have reported, not a process Expleo Group has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗What is the typical interview difficulty and preparation time?
The interview difficulty for the Data Scientist position at Expleo Group is generally considered average, requiring you to prepare thoroughly in both technical and behavioral aspects. Most candidates recommend dedicating at least 2-4 weeks for comprehensive preparation.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates often demonstrate a robust understanding of technical concepts combined with strong communication skills. They can articulate their thought process clearly and show how their experience aligns with the company's values.
PracHub interview research ↗What is the culture and working style at Expleo Group?
Expleo Group fosters a collaborative and innovative culture. Employees are encouraged to share ideas, challenge conventions, and work together towards common goals, which creates a supportive environment for personal and professional growth.
PracHub interview research ↗What is the typical timeline from initial screen to offer?
The timeline can vary, but candidates typically receive feedback within one to two weeks after the final interview. The entire process, from application to offer, usually takes around 4-6 weeks.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
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