As a Data Scientist at Slalom, you are at the intersection of advanced analytics and strategic business consulting. Unlike product-focused companies, Slalom operates as a modern consulting firm, meaning your work directly influences the strategic direction of diverse clients across various industries. You aren't just building models; you are solving complex, high-stakes problems that require both technical precision and the ability to translate data-driven insights into actionable business outcomes.
The role is inherently dynamic, often requiring you to pivot between different client domains. You will be expected to leverage your expertise in machine learning, statistical modeling, and data engineering to deliver scalable solutions. Success at Slalom requires a unique blend of "hard" technical skills and the "soft" consulting acumen necessary to build trust with stakeholders and explain complex technical concepts to non-technical audiences.
Because Slalom is a consulting firm, they prioritize candidates who demonstrate an understanding of the "consulting mindset." Show that you can not only build a model but also articulate how that model drives ROI or operational efficiency for a client.
Recruiter Screen
reportedMost 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 Deep Dive
reportedBefore anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.
What to demonstrate
- Whether an ambiguous term becomes a specific column and filter before any computation happens
- Whether you read the schema for keys and cardinality rather than only for column names
- Whether the result answers the question at the grain it was asked at, per user or per session or per day
How to prepare
- Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
- On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
- Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over
Culture and Behavioral Fit
reportedBehavioural 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.
PracHub editorial advice for the preparation topics above.
Using calendar hours, or a current rate card, as the denominator
Available hours is not days x 8. It must subtract approved leave and region-specific public holidays, scale by fte_fraction, and prorate for hire_date and termination_date inside the period; skipping this produces cross-region utilisation gaps that are purely holiday-calendar artifacts and a fake dip every time a cohort of new hires starts. Equally, bill and cost rates change over time, so joining today's dim_consultant row to last year's hours restates history. Use the rate snapshotted on the time entry, or join dim_consultant on work_date BETWEEN effective_from AND COALESCE(effective_to, 'infinity').
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.
Treating a non-significant result as proof of no effect
Say whether the confidence interval excludes the effect sizes you would have cared about. If it does not, the honest reading is that the test was underpowered, so report the minimum detectable effect the design could have found and what sample size would resolve it.
Naming a model class before naming the deployment constraints
Set out the latency budget, the label delay, the retraining cadence, the interpretability requirement and the number of labelled examples, then pick the model that fits them. A boosted-tree answer to a problem where each decision must be explained to the affected user is a well-executed answer to the wrong question.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
If a client asks for a model with 99% accuracy but the data is noisy, …
If a client asks for a model with 99% accuracy but the data is noisy, how do you manage their expectations?
Approach
- Say how the offline result would be validated online before it is trusted.
- Check what information would not exist at prediction time, and exclude it.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
Explain the trade-offs between a Random Forest and a Gradient Boosting…
Explain the trade-offs between a Random Forest and a Gradient Boosting Machine.
Approach
- Check what information would not exist at prediction time, and exclude it.
- Set a baseline first, so any model has something honest to beat.
- 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?
Build a dense consultant-week hours spine with zero weeks
You are given time_entries (time_entry_id, consultant_id, engagement_id, charge_code, work_date as datetime64, hours, is_billable, status) and a list of consultant_ids in scope plus a start and end date. Produce a DataFrame with exactly one row per consultant per Monday-anchored week in that range, with columns consultant_id, week_start, billable_hours, total_hours. Weeks in which a consultant logged nothing must appear as zeros rather than be absent. Count only rows with status == 'approved'. Do not loop over consultants and do not use resample.
Approach
- Anchor weeks arithmetically: week_start = work_date - pd.to_timedelta(work_date.dt.weekday, unit='D'). Avoid dt.isocalendar().week, whose numbers restart each January and do not sort across a year boundary.
- Construct the spine from the inputs, not from the data: pd.date_range(start, end, freq='W-MON') crossed with the in-scope consultant_ids via pd.MultiIndex.from_product, so its size is fixed before any aggregation runs.
- Aggregate approved entries once with a single groupby on [consultant_id, week_start], producing total hours and billable hours (hours.where(is_billable, 0).sum()) in the same pass.
- Left-merge the aggregate onto the spine, fillna(0.0) on both hour columns, and keep float dtypes so later ratios do not integer-divide.
- Assert the shape before returning: len(out) == n_consultants * n_weeks, and out.total_hours.sum() equals the sum of hours over the filtered input inside the window.
Worked solution 20 min
- Filter to status == 'approved' and to work_date within [start, end], then derive week_start by subtracting the weekday offset.
- Build weeks = pd.date_range(start_monday, end, freq='W-MON') and spine = pd.MultiIndex.from_product([consultant_ids, weeks], names=['consultant_id','week_start']).to_frame(index=False).
- agg = filtered.assign(billable_hours=filtered.hours.where(filtered.is_billable, 0.0)).groupby(['consultant_id','week_start'], as_index=False)[['hours','billable_hours']].sum().
- out = spine.merge(agg, how='left', on=['consultant_id','week_start']).fillna({'hours':0.0,'billable_hours':0.0}).rename(columns={'hours':'total_hours'}).
- Sort by consultant_id then week_start and run the two assertions on row count and hour conservation.
Follow-up
- Utilisation gets computed on this spine. What breaks for a consultant hired mid-window or terminated mid-window, and where would you fix it?
- How would you extend this to consultant x engagement x week without the spine exploding to a row count nobody can hold in memory?
You have three datasets with different schemas—how do you prepare them…
You have three datasets with different schemas—how do you prepare them for a predictive model?
Approach
- Say which table is the grain you start from, and join outward from it.
- 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.
Follow-up
- How does the query change if the join becomes one-to-many?
- What breaks if events arrive late or out of order?
Rebuild proposal stage timeline from a mutable event stream
fct_proposal holds proposal_id, client_id, expected_value_usd, stage, is_competitive, created_at and decided_at, and its stage column is overwritten in place. fct_proposal_stage_event holds stage_event_id, proposal_id, from_stage, to_stage and occurred_at. For proposals created in the trailing year, rebuild the ordered stage timeline with days spent in each stage, flag durations still open at the snapshot as censored, and build a funnel of distinct proposals and expected value ever reaching qualifying, scoping, submitted and decided. Reconcile each timeline's terminal stage against fct_proposal.stage and report disagreements.
Approach
- Order events per proposal and derive each stage's exit with LEAD(occurred_at) OVER (PARTITION BY proposal_id ORDER BY occurred_at, stage_event_id). The tiebreak on the event key matters because two transitions can share a second. LEAD returns NULL on the last event of every proposal, decided or not, so a NULL lead marks the end of the stream and nothing more — it is not by itself a censoring indicator.
- Close that final interval by state rather than by the NULL. Where decided_at IS NOT NULL, close it at decided_at with is_censored = FALSE; where decided_at IS NULL, close it at snapshot_date with is_censored = TRUE. Only the second group is censored, and marking it lets a consumer choose Kaplan-Meier or a restricted mean. A median computed over completed transitions alone is biased downward, because at any snapshot the still-open records are disproportionately the slow ones, and the bias grows exactly when the pipeline is slowing. A decided_at earlier than the last event's occurred_at yields a negative final interval: report those proposals, do not clamp them.
- Handle re-entry. A proposal can go scoping to submitted and back to scoping, so per-stage entries outnumber proposals. Build the funnel on COUNT(DISTINCT proposal_id) for 'ever reached stage X', and keep the per-entry rows separate for duration work.
- Build the funnel from the reconstructed timeline, never from fct_proposal.stage. That column records only where a proposal came to rest, so every proposal that passed through scoping and moved on is invisible in it and the funnel's middle collapses.
- Reconcile the last to_stage per proposal against fct_proposal.stage. Disagreements mean events are missing or a row was edited outside the event path, and the size of that set is the ceiling on how far anything else here can be trusted.
- If asked for win rate by value, exclude stage IN ('withdrawn','no_decision') from both numerator and denominator and filter is_competitive = TRUE. Those outcomes are not losses, they are not missing at random, and sole-sourced follow-on work inflates the rate.
Worked solution 45 min
- Count events per proposal and check for proposals with zero events; those exist only in the mutable table and must be reported, not dropped.
- Add the LEAD-based exit timestamp with the event-key tiebreak, then close each final interval with COALESCE(lead_occurred_at, decided_at, snapshot_date) and set is_censored only where decided_at IS NULL, so days_in_stage is defined on every row.
- Aggregate to the funnel with COUNT(DISTINCT proposal_id) and SUM of expected_value_usd per 'ever reached' stage.
- Compute the median days in stage over completed transitions only, and report the censored count beside it so the reader sees what the median excludes.
- Take the last to_stage per proposal with a window function and diff it against fct_proposal.stage, returning the mismatched proposal_ids.
Follow-up
- Estimate median proposal cycle time with the censored proposals included. What does the estimator need from your output?
- Two events for one proposal share occurred_at to the second. How does your ordering resolve it, and how would you detect that it happened?
- Which fields on a won proposal are populated only after the decision, and why does that rule them out of a win-rate model?
How would you approach a sample problem statement involving predicting…
How would you approach a sample problem statement involving predicting customer churn?
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.
- Fix the population and the time window before naming any metric.
Follow-up
- Which segment would you cut first, and what would that rule out?
- What would you do if the primary metric and the guardrail moved in opposite directions?
Walk me through a project on your resume: What was the goal, the data …
Walk me through a project on your resume: What was the goal, the data challenges, and the final impact?
Approach
- 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.
- Clarify what is being asked and what a complete answer would contain.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How would you approach a problem where the dataset is highly imbalance…
How would you approach a problem where the dataset is highly imbalanced?
Approach
- Clarify what is being asked and what a complete answer would contain.
- Work from the decision backwards to the evidence you would need.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Refuse an early stop on a weekly-monitored pilot
A 26-week pilot is running and the sponsor reads a dashboard every Monday. At week 6 the primary metric is up with a nominal p of 0.03 and the sponsor wants to roll out. Quantify the type I error those weekly looks have already spent, give the boundary the week-6 look would have had to clear under a pre-registered four-look O'Brien-Fleming design, and explain why a week-6 estimate of a newly introduced delivery process is a poor estimate of its steady-state effect in either direction. Say what you would do instead.
Approach
- Correct the naive arithmetic first. Looks at cumulative data are positively correlated, so the inflation is not 1 - 0.95^26 = 0.74. The classical repeated-significance result gives about 0.19 at ten equally spaced looks and about 0.25 at twenty, so 26 weekly looks at a nominal 0.05 sit near a quarter, roughly five times the advertised rate.
- Give the pre-registered alternative concretely. An O'Brien-Fleming boundary with four equally spaced looks uses z_k = z_K * sqrt(K/k), which yields 4.05, 2.86, 2.34 and 2.02, corresponding to nominal two-sided p of roughly 0.00005, 0.004, 0.019 and 0.043. Week 6 is about a quarter of the information, so the boundary is 4.05 and the observed p of 0.03 is z = 2.17.
- Offer the option that actually fits continuous monitoring: an always-valid confidence sequence or a mixture sequential probability ratio test, valid at every stopping time at the cost of wider intervals, rather than a fixed-horizon test read 26 times.
- Separate the statistical objection from the measurement objection. Six weeks in, treated teams are six weeks into a changed way of working. Novelty inflates early estimates through attention to anything new, primacy depresses them through resistance to a process change, and in delivery-process pilots primacy usually dominates, so the early estimate is as likely too low as too high.
- Diagnose rather than assert. Plot the effect by weeks since exposure rather than by calendar week and compare cohorts that adopted at different calendar dates. If the curve is still moving at the pre-specified end of the window, the window was too short and the estimand should be restated.
Worked solution 20 min
- Convert the reported p of 0.03 to a test statistic: two-sided, so z = 2.17.
- Construct the four-look O'Brien-Fleming boundary: choose z_4 = 2.024 so the overall two-sided level is 0.05, then z_k = 2.024 * sqrt(4/k), giving 4.05, 2.86, 2.34, 2.02.
- Place week 6 on the information fraction: 6/26 = 0.23, which is the first look, boundary 4.05.
- Compare 2.17 against 4.05: the look does not cross, so continue to the next pre-specified look rather than stopping.
- Plot the weekly effect estimate against weeks since exposure with its confidence band and check whether the curve has flattened before the pre-specified end.
Follow-up
- The sponsor argues the cost of a false positive here is low. Does that change the boundary, and how would you set alpha from a cost ratio instead of convention?
- You have four pre-registered looks. What does adding a futility boundary buy, and what does it cost?
- How would you distinguish a novelty effect from a real effect that genuinely decays?
Win rate jumped after a pipeline hygiene push
Competitive win rate by value jumped from 38% to 51% in one quarter. The metric sums expected_value_usd from fct_proposal where stage = 'won' over the same sum where stage IN ('won','lost'), filtered to is_competitive = TRUE and decided_at in the window. That quarter, partners were asked to clear stale opportunities out of the pipeline. You also have fct_proposal_stage_event. Quantify how much of the jump is selling and how much is the denominator changing shape, and state what you would monitor from now on.
Approach
- Stop looking at the ratio and plot its parts: won value and lost value by decision week, plus counts and value landing in the excluded stages 'withdrawn' and 'no_decision'. A ratio move with a flat numerator is a denominator story.
- Date the intervention and look for a spike in excluded-stage terminations around it. Those deals are not missing at random: opportunities that go stale and get cleaned up are disproportionately ones that were heading for a loss.
- Reconstruct history from fct_proposal_stage_event rather than reading the mutable fct_proposal.stage column, since the cleanup overwrote the current stage in place. Identify proposals that moved from 'submitted' or 'scoping' to 'withdrawn' after the push, and how long they had been open.
- Bound the effect: recompute win rate with withdrawn and no_decision counted as losses. The truth lies between that lower bound and the reported figure; report both numbers rather than picking one.
- Check the second lever: is_competitive is also mutable, so reclassifying a deal as sole-sourced removes it from the rate entirely. Count flips of that flag in the window.
- Propose the standing monitor: share of decided value terminating in excluded stages, published beside win rate, so the denominator can never move silently again.
Follow-up
- Some withdrawals are genuine, for example a client cancelling a procurement. How would you separate those from cleanup?
- What would you have asked for before the hygiene push to make this measurable afterwards?
- Does the same exclusion logic distort proposal cycle time, and in which direction?
For someone who has spent the last year in notebooks, dashboards or modelling work and has not written raw SQL under time pressure. The first four days rebuild query fluency against a fixture you control and can verify by hand; the last three attach that fluency to the rest of the loop.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Build a fixture you can check answers against
- Create a local Postgres or SQLite database with four tables (users, sessions, events, orders) holding roughly 200 rows you generated yourself, so you know the contents well enough to predict every result.
- Deliberately seed the cases that break queries: a user with no sessions, a session with no events, two orders sharing a timestamp, a NULL in one join key, and one duplicated user row.
- Before writing any SQL, hand-compute five answers on paper (how many users placed at least one order, median orders per ordering user, and three others) and save them as the ground truth for the week.
Deliverable: A one-command seed script plus a text file of five hand-computed answers to grade every later query against.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Joins, filters and NULL semantics
- Answer "which users have no orders" three ways (LEFT JOIN with IS NULL, NOT EXISTS, NOT IN) and confirm that the NOT IN version returns zero rows once the subquery contains a NULL, because the comparison is never TRUE.
- Reproduce the LEFT JOIN that silently collapses to an inner join by putting a right-table predicate in WHERE, then fix it by moving the predicate into the ON clause, and record both row counts.
- Create a fan-out bug on purpose by joining orders to order_items and summing the order total, then correct it with a pre-aggregated subquery and explain in one line which table changed the grain.
Deliverable: One annotated .sql file holding the three join traps, each with the wrong result and the corrected result side by side.
Practice prompt ↗Practice prompt ↗03Window functions and frames
- Write three window queries against the fixture: a running order total per user, the rank of each order within its user by value, and the day gap to that user's previous order, then check each against the day-one ground truth.
- Run ROW_NUMBER, RANK and DENSE_RANK over a column containing ties, print all three side by side, and write one sentence on when each is the correct choice.
- Switch one query from the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is what you get when ORDER BY is present and no frame is written) to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and explain why the output differs only when the ORDER BY column has duplicates.
Deliverable: Three verified window queries plus a short note explaining the RANGE versus ROWS difference in your own words.
Practice prompt ↗Practice prompt ↗04The four analytical query patterns
- Write a monthly retention grid: first order month per user, then months-since-first as the column, and verify that month zero equals the cohort size exactly.
- Sessionize the events table under a 30-minute inactivity rule using LAG plus a cumulative sum over a new-session flag.
- Build a four-step funnel that counts distinct users rather than events at each step, and state the rule you applied to a user who reaches step three without ever logging step two.
Deliverable: One file with the retention, sessionization and funnel patterns, each carrying a one-line note on the assumption it bakes in.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Write SQL the way you will have to write it live
- Set a 12-minute timer and solve three medium prompts in a plain editor with no execution and no autocomplete, then run them and tally syntax errors separately from logic errors.
- Narrate one solution aloud while writing it, stating the grain of each intermediate result (one row per user, one row per user-day) before you type its body.
- Rewrite your slowest solution as a CTE chain where every CTE name states its grain, and time yourself re-solving it from blank.
Deliverable: A recording of one narrated solution plus an error tally that separates syntax from logic.
Practice prompt ↗Practice prompt ↗06One day for everything that is not SQL
- Write the preconditions of the two-sample t-test from memory, then check them: independent observations, and a difference in means whose sampling distribution is approximately normal, which at large sample sizes follows from the central limit theorem rather than from normality of the raw values.
- Write the difference between an odds ratio from logistic regression and a relative risk, and state the condition under which the two are close (low outcome prevalence).
- Prepare a 90-second answer to "how would you know this model is any good" that names the metric, the baseline you would beat, and the cost of the errors you care about.
Deliverable: One page of notes covering test preconditions, the odds-ratio caveat and the model-quality answer.
Practice prompt ↗Practice prompt ↗07Full loop rehearsal
- Run a 45-minute mock with someone willing to interrupt: 20 minutes of SQL, 15 minutes defining a metric, 10 minutes on a past project.
- Re-solve from blank the two queries you were slowest on this week and compare the times against day five.
- Write a five-line answer to "walk me through a project" that puts a number in the first sentence and names the decision the work changed.
Deliverable: Mock feedback notes plus a timed project narrative you can deliver without reading it.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Sometimes the honest read is that the initiative did not work, and the person who commissioned the analysis was hoping otherwise. Interviewers want to know whether you softened it. Prepare the case where you delivered an unwelcome result, how you presented the uncertainty without hiding behind it, and what the team did next.
Describe a time you had to explain a complex model to a non-technical …
Describe a time you had to explain a complex model to a non-technical stakeholder.
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.
- State the situation in two sentences and spend the rest on your reasoning.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that project again?
How do you handle missing values or outliers in a production-grade dat…
How do you handle missing values or outliers in a production-grade dataset?
Approach
- Close with what you would do differently, concretely.
- State the situation in two sentences and spend the rest on your reasoning.
- Pick a story where you drove the decision, not one where you observed it.
Follow-up
- What did you decide not to do, and why?
- How did you know the outcome was caused by your change?
Scoping a one-line question about practice profitability in twenty minutes
A practice leader asks, in one line, whether the data_and_ai engagements are profitable. You have fct_engagement, fct_time_entry and fct_invoice_line, and twenty minutes before they travel. Some engagements are retainer or outcome_based, so contracted_hours is NULL; some invoices are unpaid; pursuit hours sit on charge_code = 'client_pursuit' with engagement_id NULL. Produce the scoping conversation: the questions you ask, the metric you commit to in writing, the exclusions you will have to make, and what you deliver by when.
Approach
- Name the probe: whether you convert an ambiguous one-liner into a decision with a metric attached, or start querying and return a number nobody can act on.
- Ask what changes based on the answer. Repricing the rate card, changing the staffing pyramid, and deciding whether to keep selling fixed_fee are three different questions, and they need different cuts of the same data. Pick the one the leader actually has authority over.
- Commit the metric in writing on the spot: fees plus milestone plus credit_note lines from fct_invoice_line as the revenue side, the sum of hours times cost_rate_usd over all approved entries, billable and non-billable alike, as the cost side, inception to date, reported separately by pricing_model.
- List what you will not answer and why: overrun on retainer and outcome_based work, because contracted_hours is NULL there and needs an explicit predicate rather than a silent NULL drop; cash collected, because unpaid invoices are censored; and pursuit cost, which has no engagement_id and must either be allocated on a stated rule or reported as a separate line.
- Commit two deliverables with dates rather than one vague one: the stratified margin table within two days, the mix decomposition within a week. Name the single assumption that, if wrong, flips the conclusion.
Follow-up
- They insist on one number for a partner meeting. Which single number do you give, and what sentence goes with it?
- If you had to allocate client_pursuit hours to engagements, what rule would you use and how would you show the answer is not sensitive to it?
- 01
Describe a time you had to explain a complex model to a non-technical stakeholder.
- 02
How do you handle missing values or outliers in a production-grade dataset?
- 03
A practice leader asks, in one line, whether the data_and_ai engagements are profitable. You have fct_engagement, fct_time_entry and fct_invoice_line, and twenty minutes before they travel. Some engagements are retainer or outcome_based, so contracted_hours is NULL; some invoices are unpaid; pursuit hours sit on charge_code = 'client_pursuit' with engagement_id NULL. Produce the scoping conversation: the questions you ask, the metric you commit to in writing, the exclusions you will have to make, and what you deliver by when.
Is this an official Slalom interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Slalom. Rounds and questions reflect what candidates have reported, not a process Slalom has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗Is it common to have a final "culture fit" interview?
Yes, Slalom places a high premium on team fit. This round is not a formality; it is used to assess how you handle feedback, work under pressure, and contribute to a collaborative environment.
PracHub interview research ↗How long does the process take?
It can vary significantly based on the specific office and team needs, but expect a multi-week process. It is common to have at least 3–4 rounds of interviews.
PracHub interview research ↗What is the most common reason for rejection?
Aside from technical gaps, candidates are often rejected if they cannot demonstrate "consulting experience." If you lack consulting experience, emphasize projects where you had to manage stakeholders or adapt to changing requirements.
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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2nd Order Solutions Data Scientist Interview Questions & Guide 2026
Data-quality drills for 2nd Order Solutions data scientist prep: survivorship and selection bias, leakage through time-ordered features, and extreme values where provenance decides the call. A logging fault or unit slip gets corrected or dr
York Solutions Data Scientist Interview Questions & Guide 2026
Causal-inference exercises for York Solutions data scientist prep: difference-in-differences, matching, instrumental variables, and knowing when observational data simply cannot answer the question asked.
TMC Data Scientist Interview Questions & Guide 2026
Timed practice for TMC data scientist candidates who freeze under a clock: short SQL and probability sets run end to end, then reviewed against a written rubric.
Tonal Data Scientist Interview Questions & Guide 2026
A prep plan for Tonal data scientist candidates built on spaced review: write a full answer before opening any solution, keep a running list of the assumptions you failed to state, and repeat every missed exercise three days later instead o