At UiPath, a Data Scientist serves as a bridge between complex automation capabilities and actionable business intelligence. You are not just building models; you are defining how the UiPath platform can better serve its global enterprise customers by extracting value from massive datasets and optimizing the performance of robotic process automation (RPA) workflows. This role is pivotal for scaling the company’s vision of the "Fully Automated Enterprise."
You will work within a high-growth environment where your data-driven insights directly influence product roadmaps and operational efficiency. Whether you are improving existing algorithms or designing new metrics to track user engagement, your work will directly impact how businesses worldwide deploy automation. Expect a fast-paced culture that values technical rigor, clear communication of complex concepts, and a proactive mindset toward solving ambiguous problems.
HR 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 Assessments
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
Technical Deep-Dives
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
Behavioral Discussions
reportedThis round decides whether you owned a decision or watched one happen nearby. Interviewers for data roles listen for the point where the analysis stopped being a report and started changing what someone did, so build each story around that hinge: what was going to happen by default, what you found, and what happened instead. The most common weakness is a story that ends at delivery. If you can name the decision your work changed and the number that moved because of it, most follow-ups become easy.
What to demonstrate
- Whether the decision was yours to influence, or whether you are narrating a team outcome in the first person
- The counterfactual: what would have been done without your analysis, and why that default was worse
- How far your involvement ran past the handoff, and whether you checked that the change did what you predicted
How to prepare
- Pick three projects and write one sentence for each naming the decision-maker, the choice in front of them, and what they chose after seeing your work. If you cannot name a person and a choice, the story is not ready for this round.
- Reconstruct the baseline for your strongest project from the original query or dashboard rather than memory, so the before-number survives a follow-up asking where it came from.
- Prepare an honest version of a project where your recommendation was overruled, including what you did with the analysis afterwards.
Final Decision
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
PracHub editorial advice for the preparation topics above.
Reporting a mean over accounts when account revenue is heavy-tailed
When a small number of accounts hold most of the revenue, the sample mean is dominated by whichever of them happens to be in the sample, and the sample variance keeps growing as more data arrives instead of stabilising. In that regime the usual central-limit-based confidence interval understates uncertainty, and a single renewal or a single large account's batch job can flip the sign of a measured effect. The fixes are to pre-register a winsorisation or capping rule before looking at the outcome, to report account counts crossing a threshold alongside the revenue figure, or to define the estimand on a bounded transform. Choosing the cap after seeing the result is a separate and worse problem, because the cap then encodes the answer.
Randomising an experiment at the user level when users share an account
Two problems fire at once. Colleagues in one workspace see each other's work and talk to each other, so a treated user changes the behaviour of a control user in the same account, which violates the no-interference assumption and biases the estimate toward zero. Separately, outcomes within an account are strongly correlated, so the effective sample size is roughly n / (1 + (m - 1) * rho) for m users per account and intra-class correlation rho, not n. With rho around 0.3 and twenty users per account that is a design effect near 6.7, meaning a user-level confidence interval is about two and a half times narrower than it should be and results cross significance thresholds on noise alone. Randomise the account and cluster the standard errors.
Reading a dozen metrics with no multiplicity control
Nominate one primary metric before launch and treat the rest as guardrails or exploratory, with Bonferroni or Benjamini-Hochberg applied when you intend to make claims from them. Twenty independent tests at 0.05 under the null produce at least one false positive about 64 percent of the time.
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.
How would you handle missing values in a large dataset before feeding …
How would you handle missing values in a large dataset before feeding it into a machine learning model?
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.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
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?
Implement seven-day activation rate from its written definition
Given dim_account (account_id, created_at, is_internal, is_current) and fct_api_request (account_id, request_at, http_status, api_key_id, traffic_class), implement activation_rate(accounts, requests, week_start). Definition: the numerator is accounts whose first request with http_status < 400, api_key_id not null and traffic_class != 'synthetic_monitor' occurs no later than 168 hours after created_at; the denominator is non-internal accounts created during the ISO week starting week_start. All timestamps are tz-aware UTC. Return the rate and both counts, and refuse to report a week until every account in it has had its full 168 hours.
Approach
- Reduce dim_account to one row per account_id before joining anything. It is a type 2 dimension, so several versions of the same account exist; joining the versioned table to requests multiplies the denominator by the number of plan changes an account happened to make.
- Build the denominator first and freeze it: is_internal == False and week_start <= created_at < week_start + 7 days. Everything after this is a filter on the numerator only, because an account that never sent a request must still sit in the bottom of the fraction.
- Filter requests to qualifying rows and only then take groupby('account_id').request_at.min(). The first qualifying request is not the same object as the global first request filtered afterwards, and the two answers differ for every account whose first call was a 4xx.
- Left-join the first qualifying timestamp onto the cohort and test (first_ok - created_at) <= Timedelta(hours=168). NaT propagates to False in that comparison, which is the behaviour you want, but assert it rather than assume it.
- Guard reportability explicitly: if week_start + 7 days + 168 hours exceeds the maximum request_at in the data, the week is censored and will read as a drop, so return None or raise rather than emit a number.
Follow-up
- Median time-to-first-call is more informative. What breaks if you take the median over activated accounts only, and what estimator fixes it?
- How would you decide whether 168 hours is the right window rather than 72 or 336?
- An account signs up, does nothing for 20 days, then integrates heavily. Where does it land in this metric, and is that what you want?
Permutation-test a consumption experiment randomised at account level
An experiment randomised 900 accounts into two arms. You have one row per account: account_id, arm, consumption_28d (billable units after launch) and consumption_pre (the 28 days before). Consumption is heavy-tailed and the largest account is several percent of the total. Write a permutation test from scratch: winsorise at the pooled 99th percentile as a pre-registered rule, use the difference in arm means of the winsorised outcome as the statistic, and obtain a two-sided p-value from 20,000 relabellings of the account-level arm vector. Report the observed effect, the p-value, and the same test on a CUPED-adjusted outcome.
Approach
- Be precise about what the permutation test needs. Under the sharp null of no effect for any account, the outcomes are exchangeable across arm labels, and the test is valid for ANY statistic T(outcomes, labels) provided the identical function is applied to the observed labels and to all 20,000 relabellings. The pooled 99th percentile is a function of the outcome vector alone, so recomputing it inside the loop returns the same number 20,000 times: that is wasted CPU, not a bias, and hoisting it out is an optimisation rather than a correctness fix. Say plainly that capping at all changes the estimand from mean consumption to mean capped consumption; it is not a neutral cleaning step.
- The mistake that does invalidate the test is an asymmetry between the observed statistic and the permuted ones, and the easiest way to create it is to derive the cleaning rule from the observed arm labels and then freeze it — winsorise each arm at its own observed 99th percentile, hold those two caps fixed, and permute. The observed value is then computed with caps matched to its own partition while every relabelling is scored with caps belonging to a different one, so the null distribution no longer answers the question the p-value claims to answer. A per-arm cap recomputed consistently inside every permutation is a valid test, but it estimates a contrast whose two sides are capped at different thresholds, so prefer the pooled cap on estimand grounds and pre-register it.
- Permute the account-level arm vector, because the account is the randomisation unit. Relabelling anything finer — users, workspaces, requests — generates a null distribution narrower than the design actually supports and returns p-values that are anti-conservative.
- Vectorise the null: tile the treatment indicator into a (B, n) matrix and permute along axis 1 with rng.permuted(..., out=...). The statistic is a difference of means, so the treated sum alone determines it and the whole null is one matrix-vector product. Use the two-sided p-value (1 + count(|stat_perm| >= |stat_obs|)) / (B + 1); the plus-one on each side is not cosmetic, it keeps the p-value away from exactly zero and keeps the test valid at finite B.
- For CUPED, fit theta = cov(y, x) / var(x) on the pooled data and use that same theta for the observed statistic and every relabelling. Pooled theta, like the pooled cap, carries no label information, so where in the loop you compute it is again only a performance question; fitting theta within arms is what goes wrong, because the adjusted outcome then depends on the labels and an observed-label fit frozen across all 20,000 relabellings breaks the match between observed and permuted statistics. x must be measured entirely before launch, which consumption_pre is. Expected variance reduction is about 1 - corr(y, x)^2; measure the achieved reduction from the two null distributions rather than asserting it.
Worked solution 45 min
- cap_y = np.quantile(df.consumption_28d, 0.99); y = np.minimum(df.consumption_28d.to_numpy(float), cap_y); cap_x = np.quantile(df.consumption_pre, 0.99); x = np.minimum(df.consumption_pre.to_numpy(float), cap_x)
- t = (df.arm == 'treatment').to_numpy(); n1 = int(t.sum()); n0 = len(t) - n1; obs = y[t].mean() - y[~t].mean()
- rng = np.random.default_rng(11); L = np.tile(t.astype(np.int8), (20_000, 1)); rng.permuted(L, axis=1, out=L); s1 = L @ y; stats = s1/n1 - (y.sum() - s1)/n0
- p = (1 + int(np.sum(np.abs(stats) >= abs(obs)))) / (20_000 + 1)
- theta = np.cov(y, x, ddof=1)[0,1] / np.var(x, ddof=1); y_adj = y - theta*(x - x.mean()); repeat steps 2 to 4 on y_adj and compare stats.std(ddof=1) between the two runs.
Follow-up
- The p-value is 0.04 with the cap and 0.31 without it. What do you report, and what did you pre-register?
- Colleagues in a shared workspace can see the treated behaviour. How does that change the design and the estimate?
- How many accounts would you need to detect a 5% lift given this outcome's distribution?
Write a query using SQL window functions to calculate a rolling averag…
Write a query using SQL window functions to calculate a rolling average of user activity over the last 30 days.
Approach
- Say which table is the grain you start from, and join outward from it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
Given two tables, one for user sessions and one for automation events,…
Given two tables, one for user sessions and one for automation events, how would you join them to identify the most frequent automation triggers?
Approach
- State the window function and its partition and ordering out loud before writing it.
- 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.
Follow-up
- How does the query change if the join becomes one-to-many?
- What breaks if events arrive late or out of order?
How do you ensure data quality when merging disparate datasets from di…
How do you ensure data quality when merging disparate datasets from different product modules?
Approach
- State the window function and its partition and ordering out loud before writing it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
Consecutive qualifying weeks before renewal, as a ranked worklist
fct_api_request carries account_id, workspace_id, environment, request_at (timestamptz), http_status and traffic_class. fct_subscription_period carries account_id, term_end_date, auto_renew and is_current. A qualifying week for an account is an ISO week with at least 50 successful production requests in traffic_class ('interactive','batch'). Over the last 52 whole ISO weeks, and for accounts whose current term ends within 90 days, return the current qualifying-week streak length, the week it began, and the longest earlier streak. An account whose streak has broken must appear with a current length of zero.
Approach
- Bucket weeks as date_trunc('week', request_at AT TIME ZONE 'UTC'). request_at is a timestamptz, so an unpinned date_trunc silently uses the session time zone, weeks start at a local midnight, and Monday-morning traffic lands in the previous week for part of the fleet. Pinning UTC also makes the seven-day arithmetic below exact across daylight-saving transitions.
- Apply the exclusions before counting: environment = 'production', http_status < 400, traffic_class IN ('interactive','batch'). Then apply the volume floor and drop the current partial week, which can never meet a floor calibrated on whole weeks.
- Build islands with the row-number anchor: ROW_NUMBER() OVER (PARTITION BY account_id ORDER BY week_start) as rn, then week_start - rn * interval '7 days' is constant inside a run of consecutive weeks. Group by that anchor to get each streak's start, end and length.
- The current streak is the island whose end equals the last whole week; if none does, the account's current streak is zero and that is the interesting case. The longest earlier streak is the maximum length among the remaining islands.
- Join the renewal filter from the current subscription row and LEFT JOIN the streak summary so an account with no qualifying week at all still appears, rather than vanishing from the risk list precisely because it went quiet.
- Finish with an operating point. The list is worked by a team with finite capacity, so order it and cut it at that capacity, and say what happens to the accounts below the line.
Worked solution 40 min
- Build weekly_qualified: filter the fact on environment, status and traffic_class, group by account_id and date_trunc('week', request_at AT TIME ZONE 'UTC'), keep groups with count(*) >= 50, and exclude the in-progress week and anything older than 52 whole weeks.
- Add rn = ROW_NUMBER() OVER (PARTITION BY account_id ORDER BY week_start) and anchor = week_start - rn * interval '7 days'.
- Group by (account_id, anchor) to get streak_start = min(week_start), streak_end = max(week_start), streak_len = count(*).
- Per account, take current_len as the streak_len where streak_end = the last whole week else 0, current_start from the same island, and longest_prior as max(streak_len) over the other islands.
- Join to fct_subscription_period on is_current with term_end_date <= current_date + 90, LEFT JOIN the streak summary, and order by current_len ascending then term_end_date ascending.
Follow-up
- The floor of 50 requests was picked for this exercise. How would you calibrate it from data, and what would force a recalibration?
- A regional holiday week drops several accounts below the floor at once. How do you keep that out of the risk list?
- How would you evaluate whether contacting these accounts actually changed renewal, given that coverage is assigned deliberately?
If you notice a sudden metric drop in daily active users, what is your…
If you notice a sudden metric drop in daily active users, what is your step-by-step process for diagnosing the root cause?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- 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
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
How do you balance short-term optimizations with long-term product goa…
How do you balance short-term optimizations with long-term product goals?
Approach
- Fix the population and the time window before naming any metric.
- Name one primary metric, then the guardrail that stops it being gamed.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
How would you design a product metric to measure the adoption rate of …
How would you design a product metric to measure the adoption rate of a specific automation bot?
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
- 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?
If an experiment shows a positive result but the business metrics rema…
If an experiment shows a positive result but the business metrics remain flat, how do you investigate the discrepancy?
Approach
- Say whether units interfere with each other, and switch design if they do.
- Name the guardrails that would stop a launch even on a positive primary result.
- Decide the analysis before seeing data, including how long it runs and when you look.
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 would you design an A/B test for a new feature in the UiPath Studi…
How would you design an A/B test for a new feature in the UiPath Studio?
Approach
- Say whether units interfere with each other, and switch design if they do.
- Name the guardrails that would stop a launch even on a positive primary result.
- Decide the analysis before seeing data, including how long it runs and when you look.
Follow-up
- What would you do if you could not randomise at all?
- How would you handle interference between treated and control units?
Define success for widening the free usage allowance
A proposal triples the free-tier included allowance on the requests_thousands SKU. Finance expects the count of weekly active organisations to rise. You have fct_usage_daily (billable_quantity, included_quantity_applied, overage_quantity, net_amount_cents, cogs_cents, is_restated), dim_account (account_status, is_internal, converted_to_paid_at) and fct_api_request (traffic_class, http_status, environment). Define the primary metric this change should be judged on, two guardrails, and the numeric decision rule you would agree before launch. State explicitly how the obvious metric can be satisfied without the product getting better for anyone.
Approach
- Name the mechanism before naming any metric. A larger allowance moves quantity out of overage_quantity into included_quantity_applied, so net_amount_cents falls while cogs_cents is unchanged, and accounts that previously sat under the production volume floor now cross it without paying anything. A count of active accounts therefore moves for reasons that have nothing to do with the product.
- Set the primary metric at the account grain: weekly active organisations with at least one successful request (http_status < 400) in a workspace where environment = 'production' and traffic_class in ('interactive','batch'), over accounts with is_internal = false. Report accounts that existed before launch separately from the post-launch signup cohort, so the number is not quietly an acquisition metric.
- Attach the two guardrails that close the gaming routes. First, gross margin per account, (sum(net_amount_cents) - sum(cogs_cents)) / sum(net_amount_cents) computed per account per month and reported as a distribution, specifically the count and revenue share of margin-negative accounts. Second, the free and trial share of weekly active organisations.
- Add the conversion link the change is supposed to buy: free-to-paid conversion within 90 days for the affected cohort, read from converted_to_paid_at. Without it, the primary metric rewards permanent free use indefinitely.
- Write the decision rule with numbers before launch: ship if weekly active organisations rise by at least the pre-agreed threshold, 90-day free-to-paid conversion does not fall, and the count of margin-negative accounts stays under a stated cap. Note that the margin guardrail can only be read after the metering settling window has closed, since fct_usage_daily rows restate in place.
Worked solution 20 min
- Compute per-account monthly gross margin from fct_usage_daily as (sum(net_amount_cents) - sum(cogs_cents)) / sum(net_amount_cents), grouped by account_id and calendar month, restricted to usage_date values older than the measured metering settling window.
- Simulate the allowance change on the trailing settled month: for each account, move min(overage_quantity, allowance_delta) from overage_quantity into included_quantity_applied and recompute net_amount_cents at that account's existing list and discount rates, leaving cogs_cents untouched.
- Recount weekly active organisations under the simulated state with the production volume floor applied, and split the delta into accounts that were already active and accounts newly crossing the floor.
- Report the before and after margin distribution, the count of accounts crossing into negative margin, and the revenue share those accounts hold.
Follow-up
- Weekly active organisations jump 14% in the first week after launch. How much of that can you attribute to the change with no control group, and what would you need to do better?
- An account settles at usage permanently just below the new allowance and never pays. Does your primary metric count it as a success, and should it?
Activation drops six points starting at a deploy hour
Seven-day activation, defined as an account's first request with http_status < 400, api_key_id not null and traffic_class <> 'synthetic_monitor' within 168 hours of created_at, fell six points for sign-up cohorts after a Tuesday. A client SDK major version shipped that morning. From fct_api_request (account_id, api_key_id, sdk_name, sdk_version, http_status, traffic_class, request_at) and dim_account (account_id, created_at, is_internal), decide whether activation actually fell or the metric's inputs changed, and state in advance what evidence would convince you of each.
Approach
- Decompose the definition and recompute activation under each relaxation: status only, status plus traffic_class, then the full definition. If the entire drop lives in the api_key_id clause, this is an instrumentation question rather than a behavioural one.
- Measure the null rate of api_key_id by sdk_version and by hour. A stamping change appears as a step at the deploy boundary confined to the new version; a behavioural change appears as a ramp that grows with adoption and leaves old-version traffic untouched.
- Hold the cohort's SDK mix fixed before comparing. New sign-ups adopt the newest version first, so a cohort-level drop can be pure composition even when no individual version changed at all.
- Corroborate with a source the release did not touch: whether the same cohorts appear in fct_usage_daily with non-zero billable_quantity, and whether their fct_support_ticket rows with category = 'onboarding' rose.
- Write the decision rule down before looking at the answer. An instrumentation artefact predicts unchanged downstream usage and a version-confined null step; a real regression predicts falling downstream usage and more onboarding tickets in the same cohorts.
Follow-up
- Old-version and new-version populations are not exchangeable, because new accounts adopt the new version first. How would you build a comparison that is not confounded by cohort age?
- What backfill or metric-versioning policy keeps the historical series interpretable once you fix the stamping?
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 ↗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 ↗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 ↗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 ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Saying no well is a senior skill and it is rarely rehearsed. Think of a time you told someone their analysis was not worth doing, or that the experiment could not answer their question at the sample size available. Explain what you offered instead. Refusal without an alternative reads as obstruction rather than judgement.
Describe a project where you faced significant ambiguity. How did you …
Describe a project where you faced significant ambiguity. How did you define the scope and drive it to completion?
Approach
- Quantify the outcome, including what you would not claim credit for.
- Close with what you would do differently, concretely.
- Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
Disagree with a product manager about an adoption claim
A product manager is about to present that a new SDK release drove a 40 percent rise in requests among adopting accounts, computed from fct_api_request counts grouped by sdk_version. You find the rise is concentrated in traffic_class equal to ci, that rows with is_retry true grew alongside it, and that restricting to interactive non-retry traffic leaves a 3 percent lift. The launch review is in two days. Decide how you raise this, with whom and in what order, and what you propose the claim becomes.
Approach
- The interviewer is probing whether you can correct a colleague without ambushing them, and whether your own counter-analysis carries the caveats theirs lacked. Go to the product manager privately before the review. A correction delivered in the room is a status move and loses the argument you are actually trying to win.
- Bring a decomposition rather than a verdict: the same accounts and window, requests split by traffic_class with retries held out as their own column, so their 40 percent and your 3 percent reconcile line by line and neither has to be taken on trust.
- Reproduce their figure exactly first. If you cannot land on 40 percent with their method, you do not yet know what you are disagreeing with.
- Ask whether the continuous-integration lift is itself valuable. An account wiring the SDK into its pipeline has increased integration depth, which is the dominant switching cost in this domain, so the honest claim may be that integration depth rose while interactive usage moved 3 percent. Improving the claim beats deleting it.
- Name the mechanism that makes the raw count dangerous: clients retry when the platform degrades, so retry volume climbs exactly when the customer is most at risk. Pull the 5xx rate for the same accounts and window before anyone concludes anything, and note that billable_units is zero on 5xx rows, so request counts and billable quantities diverging is itself the signal.
- Close with a standing definition for launch metrics so the next release does not repeat the exercise.
Follow-up
- The product manager argues that continuous-integration traffic is real usage and declines to split it out. Is that position defensible, and under what metric definition?
- Suppose the 5xx rate for those same accounts also rose 40 percent. What is the claim now?
- The review happens and the raw number is presented regardless. What do you do next, and what do you not do?
Announce a metric fix that cuts the headline number
Weekly active organisations, the count on the company dashboard, has never excluded rows where dim_account.is_internal is true, and it counts traffic with traffic_class in synthetic_monitor and load_test. Correcting both reduces that count by 11 percent and removes most of the growth reported over two quarters. The figure appears in a board deck and in two teams' quarterly goals, one written on the count and one on the weekly active organisation ratio, whose denominator is accounts whose account_status was in ('trial','free','active_paid') through the week. Decide the order in which you tell people, what the dashboard shows during the transition, and what you propose happens to goals already set against the old definition.
Approach
- The interviewer is probing whether you can land a correction as an operational change with a plan attached, rather than as an announcement other people then have to clean up after.
- Quantify each exclusion separately before telling anyone: internal accounts, synthetic monitors, load tests. Three known quantities are a discussion; one alarming total is an argument.
- Be precise about which side of the metric each exclusion touches, because one team's goal is on a count and the other's is on a ratio. The traffic-class filters remove requests, so they shrink the numerator only. Dropping internal accounts removes them from the ratio's denominator as well, since internal accounts carry ordinary account_status values and therefore sit in that denominator. Internal accounts are active in almost every week while the real base is not, so the numerator loses a larger share than the denominator and the ratio falls by less than the count does. Compute both and say which one the 11 percent is before anybody assumes.
- Check whether the trend changes, not only the level. A constant 11 percent shift is a rebasing and nothing more. A shift that widens over time means the reported growth was partly internal or synthetic, which makes the existing goals unachievable as written and changes what you are asking teams to do.
- Sequence the disclosure: the metric owner and the two teams whose goals move first and privately, then the board channel with a written bridge, then the dashboard. The dashboard is last because a number that changes without explanation is read as instability rather than as a fix.
- Run both series for one reporting period with the bridge visible, restate history rather than letting the series break at a date, and set the date the old series is removed.
- Propose the goal treatment yourself: rebase each target by the shift measured on the metric that target is written against, rather than leaving each team to negotiate individually, which is where corrections of this kind usually die.
Follow-up
- One team's quarterly goal is now unreachable. Rebase the target or let it miss, and what does each choice teach the organisation?
- How would this have been caught when the metric was first defined?
- What else on that dashboard shares this failure mode, and how would you find out this week?
- 01
Describe a project where you faced significant ambiguity. How did you define the scope and drive it to completion?
- 02
A product manager is about to present that a new SDK release drove a 40 percent rise in requests among adopting accounts, computed from fct_api_request counts grouped by sdk_version. You find the rise is concentrated in traffic_class equal to ci, that rows with is_retry true grew alongside it, and that restricting to interactive non-retry traffic leaves a 3 percent lift. The launch review is in two days. Decide how you raise this, with whom and in what order, and what you propose the claim becomes.
- 03
Weekly active organisations, the count on the company dashboard, has never excluded rows where dim_account.is_internal is true, and it counts traffic with traffic_class in synthetic_monitor and load_test. Correcting both reduces that count by 11 percent and removes most of the growth reported over two quarters. The figure appears in a board deck and in two teams' quarterly goals, one written on the count and one on the weekly active organisation ratio, whose denominator is accounts whose account_status was in ('trial','free','active_paid') through the week. Decide the order in which you tell people, what the dashboard shows during the transition, and what you propose happens to goals already set against the old definition.
Is this an official UiPath interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at UiPath. Rounds and questions reflect what candidates have reported, not a process UiPath has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical interviews?
The technical interviews are considered average to high in difficulty. They focus less on "gotcha" questions and more on your ability to solve practical, real-world problems using standard data science tools.
PracHub interview research ↗What is the best way to prepare for the behavioral rounds?
Focus on the STAR method (Situation, Task, Action, Result) to structure your answers. Ensure your examples highlight your contributions and how you navigated challenges within a team.
PracHub interview research ↗How much time should I spend on SQL vs. ML?
Both are important, but ensure you are very comfortable with SQL as it is frequently tested in the initial technical rounds.
PracHub interview research ↗Does the company value previous experience with automation?
While not strictly required, having a background or interest in RPA or workflow automation will certainly help you stand out.
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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