Interview guides · AKKODIS

AKKODIS Data Scientist Interview Questions & Guide 2026

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

The 60-second brief

As a Data Scientist at AKKODIS, you occupy a unique and highly impactful position at the intersection of advanced analytics, engineering, and strategic consulting. AKKODIS is a global leader in the engineering and R&D market, meaning your work will directly influence smart industry solutions across diverse sectors such as automotive, aerospace, defense, energy, and life sciences. Unlike traditional in-house data roles, your expertise will be deployed to solve complex, real-world problems for major global clients, making adaptability and technical breadth your greatest assets.

In modelling rounds the live question is usually why this model class for this problem, and how you would know six weeks after launch that it is still working. Deriving gradients by hand is rarely what is being probed.

Focus areas

Segment margin by pricing model before comparing
Compute utilisation against a defended availability denominator
Separate bookings, recognised revenue and collected cash

Overview

You will be responsible for translating massive, often unstructured datasets into actionable business intelligence and predictive models. Whether you are optimizing supply chains, developing predictive maintenance algorithms for industrial machinery, or building computer vision models for autonomous systems, your contributions will drive tangible operational efficiency. The role demands not only rigorous mathematical and programming skills but also the ability to communicate technical findings to stakeholders who may not have a data science background.

This position is ideal for professionals who thrive on variety and continuous learning. Because AKKODIS operates on a project- and client-driven model, you will have the opportunity to work on diverse tech stacks and domain challenges. Your ability to quickly understand a client's business model, identify data-driven opportunities, and deliver clean, production-ready code is what will set you apart as a high-performing Data Scientist within the organization.

The AKKODIS interview process

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

Initial Screening Call

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 and Managerial Evaluation

This round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.

What to demonstrate

Whether your row counts survive each join, and whether you notice on your own when they do not
Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
Reaching a defensible answer inside the window instead of a refined one after it

How to prepare

Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither

Final Client Interview

A day of back-to-back interviews samples your floor, not your ceiling. Four hours in, the habits that carry a good answer are the first to go: restating the question before solving it, asking what the data would have to look like, checking a number before quoting it. What the day decides is whether the tired version of you is still someone to leave alone with an ambiguous problem. The round that sinks a candidate is usually not the hardest one. It is the one immediately after the round that went badly.

What to demonstrate

Whether the late rounds get the same clarifying questions as the first one, or whether you start answering immediately to save effort
Whether a weak answer stays in the room it happened in, instead of following you into the next conversation as apology or distraction
Whether the quality of your questions holds up, since fatigue removes curiosity about the problem before it removes knowledge of the method

How to prepare

Rehearse the length, not just the content: book four mock interviews of different types in one afternoon with short gaps, because the one you need to observe is the fourth
Put the two or three questions you ask at the start of any problem on a card in front of you, so that under fatigue it is a habit you run rather than a decision you make
Decide in advance what the gap between rooms is for: water, one line of notes on anything you promised to follow up, and an explicit close on the round that just ended so it does not travel
Prepare a different closing question for each interviewer, so the end of a long day does not produce the same one four times

Where candidates lose points

Pooling margin, realisation or overrun across pricing models

Fixed-fee margin falls with hours worked; uncapped time-and-materials margin rises with hours worked; retainer margin depends on neither. A quarter in which the firm sells more fixed-fee work will show a margin change caused entirely by mix, not by delivery performance, and the aggregate can move in the opposite direction to every individual pricing model. Always stratify by fct_engagement.pricing_model before comparing periods, and report the mix shift alongside the within-stratum change.

Trending utilisation or revenue on work_date without accounting for timesheet backfill

Time entries are created days to weeks after the work happens, and the backfill tail often runs two to six weeks. A dashboard keyed on work_date therefore shows the most recent weeks as a decline that reverses on every refresh. The fix is either to hold the reporting window back past the observed backfill tail (measure the tail with the timesheet submission lag metric rather than guessing) or to report an as-of-entered_at snapshot so the series is internally consistent, and to state which one you used.

Interpreting a change before checking data quality and logging

Spend the first pass on row volume by day, null rates, duplicate keys, and whether the step change lands on a release or tracking-migration date. A discontinuity that coincides with a deploy is an instrumentation hypothesis before it is a behavioural one.

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.

Questions & practice

Walk me through a machine learning project you completed. What was the business problem, and how did you design the solution?

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Pick an evaluation metric that matches the cost of each error type, not a default.
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?

How do you address the issue of class imbalance in a classification dataset?

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?
Where could label leakage enter this setup?

Measure the timesheet backfill curve and pick a reporting cutoff

Easy · late-arriving data · data quality · cohort curves

time_entries has work_date (date), entered_at (timezone-aware UTC timestamp), hours and status. Given a snapshot_date, restrict to work_date in [snapshot_date - 180 days, snapshot_date - 60 days] so every cohort is fully observed. For k = 0..45, compute F(k): the share of a work_date cohort's final hours that already existed as of work_date + k days, pooled across cohorts. Return the 46-point curve and the smallest k with F(k) >= 0.99. Some rows are entered before the work date; those lags are real, not errors.

Approach

Compute lag = (entered_at converted to the reporting timezone and taken as a date) - work_date in whole days, then clip negative lags to 0 instead of dropping them; leave and planned time are routinely entered ahead of the work date and dropping them deflates the early curve.
Take cohort totals as groupby(work_date).hours.sum() over the restricted window. These are final only because the window stops 60 days short of the snapshot, which is why the restriction is in the prompt.
Build the numerator by summing hours per (work_date, lag), sorting by lag, taking a per-cohort cumsum, then reindexing each cohort onto the full 0..45 lag grid and forward-filling, so a cohort with no entries at a given lag holds its previous level rather than disappearing.
Pool as sum(numerators) / sum(denominators) at each k, not as the mean of per-cohort shares. Holiday weeks are tiny cohorts and would otherwise carry the same weight as a full week.
Read k* off the pooled curve and report F(45) with it: if F(45) is below about 0.995 the tail runs past the grid and k* is a lower bound, not the answer.

Worked solution (25 min)

Restrict rows to the [snapshot - 180d, snapshot - 60d] window and compute lag_days = (entered_at.dt.tz_convert(tz).dt.normalize().dt.date - work_date).dt.days, then lag_days = lag_days.clip(lower=0).
cohort_total = df.groupby('work_date').hours.sum(); by_lag = df.groupby(['work_date','lag_days']).hours.sum().
Reindex by_lag onto MultiIndex.from_product([cohorts, range(0,46)]), fill 0, cumsum within work_date to get hours_by_k.
F = hours_by_k.groupby(level='lag_days').sum() / cohort_total[cohorts_in_grid].sum(); assert F is non-decreasing.
k_star = int(F[F >= 0.99].index.min()) if any, else report 'not reached within 45 days' along with F(45).

Expected result: A monotone non-decreasing 46-point series F(0)..F(45) bounded by 1.0, plus an integer k* (or an explicit 'not reached by day 45') reported together with the value of F(45).

Verify it yourself

F is non-decreasing in k and F(45) <= 1.0, with equality only if no row in the window has a lag above 45 days.
The pooled curve lies between the minimum and maximum per-cohort curve at every k, since it is a weighted average of them.
One cohort recomputed by hand from raw rows matches the vectorised result.
Removing the clip and dropping negative lags lowers F(0); the difference equals the hours entered ahead of their work date.

Follow-up

The dashboard refreshes daily. Would you hold the window back past k*, or publish an as-of-entered_at series instead, and what does each choice cost the reader?
One practice area has a tail twice as long as the rest. Does that change the firm-wide cutoff, or does it change what you publish per practice area?

What are generator functions in Python, and how do they help with memory management when processing large datasets?

Medium · sql and data manipulation

Approach

State the window function and its partition and ordering out loud before writing it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
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?

Explain the difference between a list and a tuple in Python, and when you would use each.

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
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.

Follow-up

How would you verify this result without re-running the same query?
How does the query change if the join becomes one-to-many?

Longest consecutive bench spell per consultant from weekly activity

Hard · gaps and islands · window functions · run length

You have a dense series (consultant_id, week_start, billable_hours, full_week_leave BOOLEAN) covering 26 weeks for every billable-role consultant, with zero-hour weeks present as 0. Return every maximal run of consecutive weeks where billable_hours = 0, as consultant_id, spell_start_week, spell_end_week and week_count, then each consultant's longest run. Weeks flagged full_week_leave must neither break a run nor count toward its length. Mark runs still open at the last week of the series rather than reporting them as finished.

Approach

Remove full_week_leave weeks from the sequence first, then re-number what remains with ROW_NUMBER() OVER (PARTITION BY consultant_id ORDER BY week_start). Deleting before numbering is precisely what makes a leave week transparent to a run instead of a break in it; filtering afterwards would leave a gap in the sequence and split the spell.
Flag each surviving week as bench (billable_hours = 0) or active, then compute two row numbers per consultant: one over all surviving weeks, one partitioned additionally by the bench flag. Their difference is constant inside any run of adjacent same-flag weeks, which is the island key.
Group the bench weeks by (consultant_id, island key) and take MIN(week_start), MAX(week_start) and COUNT(*). Do not island on week_start minus an interval times a row number: that variant assumes the spine has no holes, and the leave removal has just deliberately put holes in it.
Pick the longest spell per consultant with ROW_NUMBER() OVER (PARTITION BY consultant_id ORDER BY week_count DESC, spell_start_week) so ties resolve deterministically, rather than a MAX that cannot carry the accompanying dates.
Handle both edges as censoring. A spell touching the last week of the series is still open and its length is a lower bound, so flag it; a spell touching the first week may have started earlier, so either flag it too or exclude it from any length distribution and say which.

Worked solution (40 min)

Filter out full_week_leave rows and count the surviving weeks per consultant.
Add the bench flag and the two row numbers, then print one consultant's full sequence to see the island key stay constant across a run.
Group bench weeks by consultant and island key and aggregate to spell boundaries and lengths.
Add the open-at-the-edge flag by comparing spell_end_week to the consultant's last surviving week.
Rank spells per consultant and select the longest with the deterministic tiebreak.

Expected result: One row per maximal bench run with its first and last week, its length in weeks and an is_open flag, plus a one-row-per-consultant longest-spell table. A consultant with no bench weeks appears in neither, which is correct and worth stating.

Verify it yourself

For each consultant, the sum of week_count across all spells equals their count of bench weeks in the leave-filtered series.
No two spells for the same consultant are adjacent in the re-numbered sequence; adjacency would mean the island key was computed wrongly.
The longest-spell table has at most one row per consultant and its week_count matches the maximum in the spells table.
Hand-check one consultant by printing their 26 weeks as a strip of zeros, hours and leave markers, and reading the runs off it.

Follow-up

A spell begins before your 26-week window. How do you detect that, and what do you report as its length?
Rebuild this at daily grain. What changes about weekends, holidays and the island key?
How would you separate true bench from unbilled delivery work on a live engagement, given only charge codes?

How do you prioritize tasks when working on multiple client deliverables simultaneously?

Medium · product sense and metrics

Approach

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.
Fix the population and the time window before naming any metric.

Follow-up

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

How do you optimize Python code for performance when working with large-scale arrays?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
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.

Follow-up

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

Can you explain the technical architecture of your university thesis project and how Python was utilized?

Medium

Approach

State your assumptions explicitly before working the problem.
Work from the decision backwards to the evidence you would need.
Say what you would check first and why it is the highest-information step.

Follow-up

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

Define success for a rate-card increase before any rollout

Hard · pricing · decision threshold · clustering · power

A 6% rate-card increase is proposed for one practice area next quarter. Before it ships, define what success means. Available: fct_proposal (expected_value_usd, stage, loss_reason, is_competitive, submitted_at, decided_at, practice_area), fct_engagement (pricing_model, contract_value_usd, practice_area, client_id) and fct_invoice_line (line_type, amount_usd, billed_hours). Name the primary metric, the guardrail that genuinely opposes it, the unit of analysis, and the numeric threshold that would reverse the decision. State how many account-quarters are needed to detect an effect large enough to change the decision, and what you do if that exceeds what the practice has.

Approach

Make the primary realised revenue per billable hour, not the list rate. The increase can be conceded straight back through discount and show up as no change at all. Numerator: SUM(amount_usd) on line_type IN ('fees','milestone'). Denominator: SUM(billed_hours) over the same lines.
Name the opposing guardrail: value-weighted competitive win rate in that practice area, with the share of losses carrying loss_reason = 'price' as the supporting diagnostic. Raising price lifts the primary and depresses the guardrail by construction, so the rollout pays off only if revenue per hour rises faster than volume falls.
Derive both break-evens before looking at outcome data. Revenue is preserved while the volume loss stays under 6 / 106, roughly 5.7%. For margin, write p for realised price per billable hour and c for avoidable cost per billable hour, and require c < p so the baseline margin is positive. Setting post-change margin (1 - L) x (1.06p - c) equal to pre-change margin (p - c) gives 1 - L = (p - c) / (1.06p - c), so the break-even volume loss is L = 0.06p / (1.06p - c). That is strictly above 5.7% whenever c > 0, collapses to 5.7% at c = 0, and stays below 100% as long as c < p; a practice already below cost at the old rate is not a pricing question. It holds only where c is genuinely avoidable on the volume you lose; on a salaried bench that nobody stands down or resells, no cost is saved and the margin break-even falls back to the revenue one. Report both ends and name the assumption the threshold uses.
Fix the unit and confront the identification problem. Assignment is at practice_area while the outcome sits at engagement or account, so cluster at client_id. With one treated practice area there is a single treated cluster and a conventional difference-in-differences is not identified; propose a synthetic control over the other practice areas with the donor-pool condition stated, or a pre-registered before-and-after whose parallel-trends check runs on the pre-period with a named failure action.
Run the power calculation before proposing the rollout and be willing to conclude the practice cannot detect the effect. Competitive decisions in one practice area number in the tens per quarter, so a 3-point win-rate move needs many quarters. If the requirement exceeds what exists, the honest outputs are a staged rollout with a pre-committed stopping rule, or shipping without measurement and saying so, not a manufactured p-value.

Worked solution (40 min)

Compute realised revenue per billable hour by practice-quarter for eight pre-period quarters from fct_invoice_line fees and milestone lines over billed_hours.
Compute value-weighted competitive win rate per practice-quarter, cohorted on decided_at, denominator stage IN ('won','lost'), is_competitive = TRUE.
Derive the two break-even volume losses: 6 / 106 for revenue, and 0.06p / (1.06p - c) for margin using the practice's realised price and avoidable cost per billable hour, then recompute with c treated as unavoidable to get the other end of the range.
Count competitive decisions per quarter in the practice and compute the minimum detectable win-rate change at 80% power from a two-proportion test, treating that as a floor because clustering raises the real requirement.
Write the rule: threshold, horizon, stopping condition, and the action taken when the power calculation says the effect is undetectable.

Expected result: Primary metric realised revenue per billable hour; opposing guardrail value-weighted competitive win rate. Revenue break-even volume loss 6 / 106, roughly 5.7%. Margin break-even 0.06p / (1.06p - c), strictly larger than 5.7% for any positive avoidable cost per hour and equal to 5.7% when that cost is zero or unavoidable; the reversal threshold sits in that range with the avoidability assumption stated. Unit of analysis engagement or account, clustered at client_id, with the single treated practice area named as the identification problem and a synthetic control or pre-registered before-and-after proposed in response.

Verify it yourself

With c < p, the margin break-even volume loss sits between 5.7% and 100%, equalling 5.7% exactly at c = 0. If you have written (p - c) / (1.06p - c) you are holding the retained-volume fraction, the complement of the answer: at c = 0 it returns 94.3% rather than 5.7%.
The power calculation runs on the count of competitive decisions, not on engagements or invoice lines, since the win-rate guardrail is what triggers the reversal.
The parallel-trends check is specified with a stated failure action, not merely mentioned as a caveat.

Follow-up

Realised revenue per hour rose 4% and win rate fell 2 points on 31 competitive decisions. What do you conclude, and what is your interval on the win-rate move?
How would you separate a price-driven loss from a capability-driven one when loss_reason is entered by the partner who lost the deal?

Realisation rate steps up overnight after a warehouse release

Medium · definition change · realisation · restatement

Realisation rate for one practice area steps from 0.93 to 1.02 on a single reporting date. Bill rates, hours worked and the client set are unchanged. The metric is SUM(amount_usd) from fct_invoice_line over SUM(hours * bill_rate_usd) from approved billable fct_time_entry rows for the same engagement and period. A warehouse release shipped that week. Determine what in the definition can produce a level shift with no operational change, and deliver a back-restated series plus the definition you would keep.

Approach

Treat the shape as evidence: a definition change is a discontinuity at one processing boundary, while a behaviour change ramps over weeks. Plot daily, not monthly, to see which one this is before forming any hypothesis.
Diff the metric's lineage across the release and enumerate the candidate predicates: which line_type values enter the numerator (dropping credit_note and write_off inflates it), whether the period join moved from period_start_date to issued_at, and whether the denominator counts status = 'approved' only or now includes 'submitted'.
Run both definitions over the same fixed pre-release month. If old and new differ by approximately the size of the step, the step is definitional; if they agree, look for a behaviour change coinciding with the release date.
Sanity-check the new level against the metric's physics: values above 1.0 are legitimate only on fixed_fee work delivered under its hour budget, so a practice dominated by time_and_materials sitting at 1.02 is itself a red flag.
Restate the full history under the chosen definition, publish old and new series together with the changeover marked, and add a reconciliation test that recomputes one known engagement-month from raw rows.

Follow-up

Which definition is correct, and what does excluding write-offs from the numerator do to the guardrail relationship with utilisation?
How do you prevent the next release from silently restating a headline metric?
If the release also changed the denominator's status filter, which direction does that push realisation and by roughly how much?

Suggested practice checklist

Roughly 90 minutes a night on weekdays with one longer weekend block. The plan deliberately cuts scope rather than compressing everything, on the assumption that finishing one thing a night beats half-starting four.

Day 1 — Fix the scope and set a baseline

Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list for everything else and keep it visible all week.
Take one 20-minute SQL prompt and one 10-minute metric question cold, and write the single sentence that says what blocked each attempt, since that sentence is what decides which two topics get the most evenings.
Set the week's one rule: one problem finished to completion every night, including the night you only have 40 minutes.

Deliverable: A one-page scope with an explicit not-doing list and two cold attempts, each carrying one sentence on what blocked it.

Day 2 — One query pattern, written three times

Choose the single pattern most likely to appear (a cohort retention grid, or a funnel counted by user) and write it three times from a blank file rather than editing the previous attempt.
On the third attempt, write the grain of every CTE as a comment before writing its body.
Stop at 90 minutes even if the third version is imperfect, and write the one thing you would fix with another hour.

Deliverable: Three independent versions of the same query plus a note on what changed between them.

Day 3 — Only the statistics you will be asked to defend

Write, in under 200 words, how you would decide whether a difference between two groups is real: the test, its assumptions, and what you would switch to when an assumption fails.
Compute a 95 percent confidence interval for a difference in proportions by hand on realistic numbers, then write in one sentence what changes if the two samples are paired rather than independent.
Write your answer to "what does a p-value mean", check it against a definition, and delete the version that describes it as the probability the hypothesis is true.

Deliverable: A 200-word written answer and one hand-computed interval you can reproduce under pressure.

Day 4 — One case, and the assumptions holding it up

Answer one product case aloud in 20 minutes with a recording running, then listen back with a pen and mark every claim you asserted without saying what it rested on: an assumed user behaviour, an assumed data source, an assumed baseline rate, an assumed grain.
Pick the three assumptions the recommendation actually depends on, write how you would check each one against data, and say which one being wrong would flip the recommendation rather than merely weaken it.
Write the four-step structure you used onto a card small enough to hold in working memory when you are nervous.

Deliverable: One recording, three load-bearing assumptions each with a written check, and a four-step structure card.

Day 5 — Your own work, timed

Write a 90-second version and a four-minute version of your main project, and time both out loud rather than reading them.
Prepare answers to the two follow-ups that always come: what you would do differently, and how you knew it worked.
Put one number in the first sentence and be able to say exactly where that number came from and what it excludes.

Deliverable: Two timed narratives with one defensible number in the opening line.

Day 6 — The one full rehearsal, in a longer weekend block

Run a 60-minute mock covering query work, a case and a behavioural question in a single sitting with no breaks, because sustained attention is the thing evenings have not trained.
Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.

Deliverable: Mock notes naming three failure moments with a specific fix written under each.

Day 7 — Taper

Write the 20-minute warm-up you will actually do on the morning of the interview: one query you can already write from a blank file, one metric you can define out loud, and nothing you have never seen before.
Re-read only your own notes from this week, and open no new material.
Write down the logistics: the tool you will be asked to work in, whether lookups are allowed, and the sentence you will use when you do not know something.

Deliverable: A one-page card holding the case structure, the project numbers, and the logistics.

Where your data came from

Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.

Describe a time when a project's requirements changed midway through development. How did you adapt?

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
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.

Follow-up

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

How do you handle missing or corrupted data in a large dataset using Pandas?

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.
Name the disagreement or constraint, and how you resolved it with evidence.

Follow-up

What did you decide not to do, and why?
How did you know the outcome was caused by your change?

Defending your own impact claim without randomisation or clean units

Hard · self-selection · clustered inference · impact measurement

At your review you plan to claim that the realisation dashboard you built recovered 1.4 million dollars. The evidence is that engagement-month realisation rose four points over two quarters among engagements whose leads used it. Adoption was voluntary. There are about sixty client accounts and the top five carry most fees. A new rate card shipped in the same quarter. Write the claim you can defend, the estimate you would actually produce, and what you say when asked for a causal number you cannot get.

Approach

Name the probe: whether you can separate the number you want from the number the data supports, under review pressure, without either inflating it or retreating to saying nothing can be known.
State both identification problems concretely. Voluntary adoption means adopting leads are plausibly the ones who already manage realisation, so the comparison is confounded at the person level. The rate card changes bill_rate_usd, which sits in the realisation denominator, so part of the four-point move is arithmetic rather than behavioural.
Neutralise what you can. Recompute realisation with bill rates snapshotted on work_date, or hold the denominator at the old rate card, so the rate-card change cannot move the metric by construction. Then rerun the comparison.
Get the inference right for the unit count. Cluster at client_id, not engagement, because engagements in one account share a partner, a rate card and a team. With sixty accounts and five carrying most fees, the effective cluster count is far below sixty, so report a wild cluster bootstrap interval rather than plain cluster-robust standard errors, which are biased downward in that regime.
Report both weightings and explain the divergence: an account-weighted estimate describes the typical account, a value-weighted one describes the revenue, and if they disagree a small number of accounts is carrying the result. Then give the decision-relevant sentence: the defensible range, whether its lower bound still clears the build cost, and what a proper staggered rollout would have bought.

Follow-up

The pre-period trends for adopters and non-adopters are not parallel. What do you report then?
You get to design the next rollout. What do you change so the same question is answerable, without randomising individual accounts?