As a Data Scientist at Tiger Analytics, you occupy a key strategic role at the intersection of advanced quantitative modeling, data engineering, and enterprise consulting. Tiger Analytics is a global leader in AI and analytics consulting, partnering with Fortune 500 enterprises to solve complex, high-impact business problems. In this role, you will not merely build predictive models in isolation; you will design end-to-end data-driven solutions that directly influence major operational and strategic decisions for clients across retail, financial services, healthcare, and technology sectors.
Your day-to-day work spans the complete analytical lifecycle. You will extract and manipulate massive, unstructured or structured datasets, formulate structured mathematical hypotheses for ambiguous business questions, and deploy machine learning, econometrics, or optimization algorithms into production cloud environments. Whether you are engineering Marketing Mix Models (MMM) to optimize multi-million dollar ad spending, building customized natural language processing pipelines, or architecting supply chain optimization algorithms in platforms like and, your work directly drives revenue and operational efficiency for global brands.
What makes this role uniquely compelling is its dual nature: high technical depth combined with strategic consulting exposure. You will work alongside domain experts, data engineers, and executive stakeholders. To succeed, you must demonstrate technical mastery in algorithms and programming alongside the executive presence required to translate complex statistical outputs into actionable recommendations.
HR Recruiter Screening
reportedData Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.
What to demonstrate
- Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
- Whether you name what you have not done instead of stretching to cover every line of the posting
- Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage
How to prepare
- Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
- Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
- Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live
Technical Assessment
reportedA handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.
What to demonstrate
- Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
- Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
- Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.
How to prepare
- Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
- Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
- If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows
Live 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
Project Review Interview
reportedRounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.
What to demonstrate
- Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
- The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
- Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options
How to prepare
- Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
- Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
- Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.
Final Rounds
reportedA loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.
What to demonstrate
- Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
- Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
- Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience
How to prepare
- Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
- For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
- Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story
1 candidate reports. Individual accounts describe a particular role and hiring cycle.
Software Engineer interview at Tiger Analytics
The technical questions leaned toward data engineering. I had two technical rounds on Python and PySpark coding, moderate-difficulty DSA, and Azure Data Factory, so I had to discuss data workflows as well as algorithms. The questioning sometimes moved quickly and felt argument-driven. I did not feel I had much time to write or iterate on query-like work, which made it harder to show the backend d…
Read full experiencePracHub editorial advice for the preparation topics above.
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.
Treating accounts as independent observations
Revenue is concentrated: a small number of client_ids typically carries a large share of fees, and engagements within one account share a partner, a rate card and a delivery team. Ordinary standard errors computed over engagements therefore understate uncertainty badly. Cluster at client_id, and with fewer than roughly 40 clusters use a wild cluster bootstrap or a CR2 correction, because cluster-robust standard errors are downward-biased in that regime and will manufacture significance that a replication will not reproduce.
Ending an analysis without a recommendation or next step
Close with what you would do and what would change your mind, stated as a condition you can check later. If the evidence is genuinely inconclusive, recommend the specific next measurement and say what it costs in time or exposure.
Ignoring interference between units in a marketplace experiment
Ask whether one unit's treatment can change another unit's outcome through shared inventory, a matching pool, a social graph or a common budget. Where it can, randomise at a level that contains the spillover, such as region or time slice, and say explicitly what that costs you in statistical power.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Walk me through a probability puzzle: What is the probability of drawi…
Walk me through a probability puzzle: What is the probability of drawing two cards of the same suit sequentially from a standard 52-card deck without replacement?
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Translate the result into the decision it informs, in one plain sentence.
- Say what the estimate is of, and over what population it generalises.
Follow-up
- Which assumption here is most likely to be violated in practice?
- How would you explain this result to someone who does not know statistics?
A major client wants to reduce customer churn across their subscriptio…
A major client wants to reduce customer churn across their subscription platform. Walk me through your step-by-step framework from problem definition and feature engineering to deployment and business tracking.
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Pick an evaluation metric that matches the cost of each error type, not a default.
- 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?
Collapse time entries into contiguous staffing spells
From approved delivery time entries (consultant_id, engagement_id, work_date, hours, charge_code, status, with engagement_id not null), build staffing spells. Per consultant and engagement, collapse weeks containing any logged hours into contiguous runs, where three or more consecutive zero-hour weeks end a spell. Output one row per spell with consultant_id, engagement_id, start_week, end_week, active_weeks, gap_weeks and total_hours. Consultants sit on several engagements at once, so spells from different engagements may overlap in time and must not be merged. No Python loop over rows.
Approach
- Aggregate to (consultant_id, engagement_id, week_start) with summed hours and keep only weeks with positive hours. The absent weeks are the signal, so materialising zeros here would destroy the thing you are detecting.
- Convert week_start into an integer week index, ((week_start - epoch_monday).dt.days // 7), so gap detection is integer subtraction rather than calendar arithmetic that breaks over month and year boundaries.
- Sort by [consultant_id, engagement_id, week_index], diff the week index inside each pair, and mark a spell start where the diff is null (first row of the pair) or greater than 3. A diff of 1 is adjacent weeks and a diff of 3 is two empty weeks, which the tolerance permits.
- Take spell_id = the cumulative sum of that boolean over the whole frame so ids are globally unique, then a single groupby on [consultant_id, engagement_id, spell_id] yields min and max week, active week count and summed hours; gap_weeks = (end - start + 1) - active_weeks.
- Do not deduplicate overlapping spells across engagements. A consultant on two engagements in the same week is the normal case, and that overlap is the fact any capacity or context-switching question needs.
Worked solution 30 min
- Filter to status == 'approved', charge_code == 'client_delivery' and engagement_id.notna(); derive week_start by subtracting the weekday offset from work_date.
- wk = df.groupby(['consultant_id','engagement_id','week_start'], as_index=False).hours.sum(); wk = wk[wk.hours > 0]; wk['wi'] = (wk.week_start - pd.Timestamp('1970-01-05')).dt.days // 7.
- wk = wk.sort_values(['consultant_id','engagement_id','wi']); d = wk.groupby(['consultant_id','engagement_id']).wi.diff(); wk['new_spell'] = d.isna() | (d > 3); wk['spell_id'] = wk.new_spell.cumsum().
- spells = wk.groupby(['consultant_id','engagement_id','spell_id']).agg(start_week=('week_start','min'), end_week=('week_start','max'), active_weeks=('wi','size'), total_hours=('hours','sum'), span=('wi', lambda s: s.max() - s.min() + 1)).reset_index(); gap_weeks = span - active_weeks.
- Run the conservation assertion (spell hours sum to input hours) and the toy case below before returning.
Follow-up
- Re-run with a one-week and a four-week tolerance. What happens to the spell count, and which tolerance would you defend to a staffing lead?
- Using these spells, how would you measure how many engagements a consultant is split across in a given week, and why is that not just a count of rows?
Given a table of customer transactions, write a query using SQL window…
Given a table of customer transactions, write a query using SQL window functions to calculate the 7-day rolling average spend per user and rank their top purchasing categories.
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Say which table is the grain you start from, and join outward from 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?
In pandas, how do you perform efficient mean imputation across grouped…
In pandas, how do you perform efficient mean imputation across grouped records without causing data leakage across training and test splits?
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Say which table is the grain you start from, and join outward from 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?
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?
Imagine a key business metric drops by 15% overnight. Walk me through …
Imagine a key business metric drops by 15% overnight. Walk me through your systematic root-cause diagnosis to isolate whether it is a technical data bug or a real product issue.
Approach
- 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.
- State what result would change your recommendation, so the answer is falsifiable.
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?
Walk us through how you would design a dynamic pricing engine for an e…
Walk us through how you would design a dynamic pricing engine for an e-commerce platform during peak holiday sales.
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?
- How would you detect that the metric is being gamed rather than genuinely improving?
When embarking on an analytical project with vague requirements from a…
When embarking on an analytical project with vague requirements from a non-technical client, how do you define success criteria and establish baseline metrics?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Name one primary metric, then the guardrail that stops it being gamed.
- Fix the population and the time window before naming any metric.
Follow-up
- 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 would you structure product metric design for an enterprise SaaS d…
How would you structure product metric design for an enterprise SaaS dashboard to monitor active engagement and platform health?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- 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.
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?
Describe common experimentation pitfalls such as novelty effects, netw…
Describe common experimentation pitfalls such as novelty effects, network interference, and p-hacking, and explain how you mitigate them.
Approach
- Decide the analysis before seeing data, including how long it runs and when you look.
- Say whether units interfere with each other, and switch design if they do.
- 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?
How do you calculate the required sample size and minimum detectable e…
How do you calculate the required sample size and minimum detectable effect for an A/B testing initiative on a high-traffic web page?
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- State the primary metric and the minimum effect worth shipping, then size the test.
- Decide the analysis before seeing data, including how long it runs and when you look.
Follow-up
- How would you handle interference between treated and control units?
- What would you do if you could not randomise at all?
Define competitive win rate for a quarterly pipeline review
Pipeline reporting currently shows win rate as won proposals divided by all proposals created. Using fct_proposal (proposal_id, expected_value_usd, stage, loss_reason, is_competitive, created_at, submitted_at, decided_at), write a definition you would defend to a finance lead: numerator, denominator, window and every exclusion. Decide whether to weight by count or by expected_value_usd, and justify the choice. State what happens to proposals still in 'qualifying', 'scoping' or 'submitted' at the snapshot date, and why dropping them is not a neutral act.
Approach
- Cohort on decided_at, not created_at. A created_at cohort mixes decided and undecided proposals, so the rate for a recent quarter is computed over whichever deals resolved fastest and drifts upward on every refresh.
- Set the denominator to stage IN ('won','lost'). Excluding 'withdrawn' and 'no_decision' is defensible only if you report their count and value alongside, because they are not missing at random: they skew toward deals that were heading for a loss.
- Filter is_competitive = TRUE. Sole-sourced follow-on work closes at near certainty and mechanically inflates the rate, so it belongs on its own line rather than inside the headline number.
- Report count-weighted and value-weighted side by side. They diverge when large proposals lose more often than small ones, which is the diagnostically useful case; the value-weighted version is the one that ties to revenue.
- Treat open proposals as right-censored rather than absent. Either restrict to cohorts old enough that most proposals have decided, picking that horizon off a Kaplan-Meier cycle-time curve, or publish the rate with the undecided count and value attached to it.
Worked solution 25 min
- Compute the naive rate (won / all created) and the proposed rate (won / won+lost, is_competitive = TRUE, cohorted on decided_at) for the same four quarters.
- For each quarter, count and sum the value of proposals in 'withdrawn' and 'no_decision', and of proposals still open at the snapshot.
- Compute the value-weighted version and compare it with the count-weighted one.
- Re-run the most recent quarter as it would have looked 30 days ago and record how much each definition moved.
Follow-up
- Win rate rose four points while proposal volume fell by a third. Which do you report first, and what single query separates a genuine quality shift from a volume shift?
- How would you detect partners reclassifying likely losses as 'withdrawn' to protect the rate?
Two-year-old engagement margins moved after a dimension join change
After a warehouse release, gross margin on engagements closed two years ago changed by up to six points, upward on some and downward on others. The cost model now joins dim_consultant on consultant_id AND is_current = TRUE to read standard_cost_rate_usd, instead of using the cost_rate_usd already snapshotted on each fct_time_entry row. Available columns include consultant_sk, consultant_id, level, standard_cost_rate_usd, effective_from, effective_to, is_current, termination_date. Explain the restatement, quantify who it favours, and specify the join you would ship.
Approach
- State the mechanism in one line: joining a type-2 dimension on is_current pastes today's cost rate onto historical hours, so every consultant promoted or re-rated since restates their own history in the direction their rate moved. Because promotions differ per person, engagement margins move in both directions, which is exactly the observed pattern.
- Quantify with three parallel cost computations per engagement: (a) the snapshotted fct_time_entry.cost_rate_usd, (b) an effective-dated join using work_date BETWEEN effective_from AND COALESCE(effective_to, 'infinity'), and (c) the shipped is_current join. Report the distribution of (c) minus (a) and correlate it with the share of hours delivered by staff who have since been promoted.
- Check cardinality as a separate defect: an is_current join can drop consultants whose current row is missing or whose termination retired the row, and can fan out if consultant_id carries more than one current row. Compare hour totals before and after the join; they must be identical.
- Decide the canonical source and justify it: the snapshotted rate on the time entry is what was booked and is authoritative for financial restatement. The effective-dated join is the reconciliation, and a systematic disagreement between (a) and (b) is its own defect worth raising rather than papering over.
- Reissue the affected periods with an explicit changeover note, and pin the rule in a test that fails if is_current appears in any join from a fact table to a type-2 dimension on a historical grain.
Follow-up
- Some entries have cost_rate_usd NULL or zero. Which source wins then, and what does that do to your reconciliation?
- The same pattern exists for bill_rate_usd. What does it do to realisation, and in which direction?
- How would you sweep every model in the warehouse for this class of bug without reading each one?
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.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix 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.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02One 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.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Only 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.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04One 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.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Your 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.
Practice prompt ↗Practice prompt ↗06The 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.
Practice prompt ↗Practice prompt ↗07Taper
- 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.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Most data work is done by groups, so an interviewer has to work out which piece was yours. An answer that runs on 'we' for several minutes gets interrupted with a question about what you personally did, and by then the answer sounds defensive even when it is true. Mark your own contribution as you go, and name the parts that belonged to someone else instead of leaving them ambiguous. Keep a few specifics back as well, like the name of the metric or who actually objected, so a probe can be answered with something you had not already said.
Tell me about a data science project that failed or did not meet its i…
Tell me about a data science project that failed or did not meet its initial performance benchmarks. What went wrong, and what did you learn?
Approach
- Close with what you would do differently, concretely.
- Pick a story where you drove the decision, not one where you observed it.
- Quantify the outcome, including what you would not claim credit for.
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
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?
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
Tell me about a data science project that failed or did not meet its initial performance benchmarks. What went wrong, and what did you learn?
- 02
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.
- 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 Tiger Analytics interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Tiger Analytics. Rounds and questions reflect what candidates have reported, not a process Tiger Analytics has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the technical assessment at Tiger Analytics?
The initial online assessment is moderately challenging and time-sensitive. It tests both core Python programming (data structures, string manipulation, array traversal) and intermediate-to-advanced SQL query writing, alongside multiple-choice questions on statistics and ML theory. Consistent practice on standard coding platforms will prepare you well.
PracHub interview research ↗How long does the hiring process typically take?
The interview process generally takes between two to four weeks from the initial recruiter phone screen to the final offer stage. However, timelines can vary depending on client project alignment and scheduling requirements across global teams.
PracHub interview research ↗What sets successful candidates apart during the interview process?
Successful candidates distinguish themselves through structured problem solving and business context. Rather than jumping straight into complex models, high-scoring candidates clarify assumptions, establish simple baselines, discuss data constraints, and explain how their model directly drives business value.
PracHub interview research ↗Are the interviewers primarily technical or business-focused?
You will encounter both. Early rounds are conducted by senior data scientists and technical leads who evaluate coding speed, statistics, and ML mechanics. Later rounds are conducted by Directors, Vice Presidents, or client leads who assess strategic thinking, business intuition, and communication skills.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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