As a Data Scientist at Uptake, you will play a pivotal role in transforming complex datasets into actionable insights, driving decision-making across the organization. Your work will directly influence the development of innovative products that enhance operational efficiency for clients, particularly within industries that rely on data-driven solutions, such as transportation, manufacturing, and energy. You will engage with cross-functional teams to tackle real-world challenges, utilizing advanced statistical methods and machine learning algorithms to derive insights that lead to strategic improvements.
The impact of your contributions will resonate throughout the company, helping to shape analytics capabilities and enhancing the value offered to clients. You will be part of a collaborative environment where your analytical expertise fuels the creation of predictive models and analytics tools that empower clients to make informed decisions. This role is critical not just for its technical aspects, but also for its strategic influence on business outcomes, making it both challenging and rewarding.
In this dynamic setting, you will work on diverse projects that require a deep understanding of data science principles, statistical modeling, and algorithm development. The complexity of the datasets you will handle, combined with the need for innovative solutions, makes this position both exciting and essential for the growth of as a leader in data analytics.
Phone 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 Interview
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
Take-Home Project
reportedThe clock is part of the test. Three to six hours is not enough to do everything the dataset supports, so the submission mostly reveals how you spend a fixed budget against an open question. A reviewer sees which paths you took and, by absence, which you abandoned. Work that runs out of time inside the analysis ships a thin conclusion, while work that cuts scope early protects the last hour for writing. The most reliable way to lose here is to leave the scoping decision implicit, so it reads as something you missed rather than something you chose.
What to demonstrate
- Whether the scope you settled on is presented as a decision with a reason, rather than left for the reader to infer from what is missing
- Whether the depth of the work is consistent with the stated time budget, instead of several half-finished directions left open
- Whether the closing section reads as something written on purpose rather than assembled from whichever cells survived
How to prepare
- Run a timed rehearsal on a public dataset with a hard stop, holding the final sixty minutes for writing no matter where the analysis has got to
- Before opening the data, list the questions it could plausibly answer, pick one, and keep the discarded ones as a short note on what you did not attempt and why
- Commit a one-line finding after each analysis step so the writeup is assembled from recorded results rather than from memory at midnight
Presentation
reportedAn extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.
What to demonstrate
- Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
- Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
- How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one
How to prepare
- Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
- Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
- Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.
PracHub editorial advice for the preparation topics above.
Collapsing cancellation and payment failure into one churn number.
Involuntary churn from expired or declined payment instruments is a large and volatile share of gross churn, and it responds to retry schedules, card-updater coverage and billing provider, not to anything in the product. It also resolves late, so a period that looks involuntary today can be a successful retry next week, and reading the split before the dunning horizon closes overstates it. Compounding this, cancel-at-period-end means the cancellation request and the entitlement end are different timestamps on different rows, so a churn curve keyed on cancel_requested_ts and one keyed on churn_ts disagree by a full billing period.
Testing hours, revenue or completion with a difference in means on a heavy-tailed distribution.
Listening and viewing hours per account are strongly right-skewed and content popularity is close to power-law, so the variance of a sample mean is dominated by a few accounts and the central limit approximation converges slowly at realistic sample sizes. A t-test on mean hours can flip sign when one heavy account's week changes, and an experiment can appear significant because a single title released into one arm's window. Capping at a pre-registered percentile, or decomposing into a rate (did they stream at all) and a conditional intensity, controls the variance, at the stated cost that capping biases toward zero exactly when the true effect lives in the tail.
Averaging per-user rates to produce a population rate
Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.
Accepting a metric definition without asking about the denominator
Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function to calculate the mean and standard deviation of a dat…
Write a function to calculate the mean and standard deviation of a dataset.
Approach
- Sanity-check the answer against a simple bound or a simulated case.
- 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
- How would you explain this result to someone who does not know statistics?
- What sample size would you need to detect an effect half this size?
Explain the concept of overfitting and how you would prevent it in a m…
Explain the concept of overfitting and how you would prevent it in a machine learning model.
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Check what information would not exist at prediction time, and exclude it.
- Pick an evaluation metric that matches the cost of each error type, not a default.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- Where could label leakage enter this setup?
Attribute streams to prior impressions without merge_asof
You have impressions (impression_id, profile_id, content_version_id, rendered_at, surface, slate_position, viewport_visible_ms) and streams (stream_id, profile_id, content_version_id, started_at). Attribute each stream to the most recent impression of the same profile and content version with rendered_at at or before started_at, a gap of at most 30 minutes, and viewport_visible_ms above zero; break ties on rendered_at by the smaller slate_position. pd.merge_asof and any groupby-apply over individual rows are off limits. Return streams with impression_id added, plus the attributed share by surface.
Approach
- Filter impressions to viewport_visible_ms above zero first. A row that never scrolled into view is not an exposure, and leaving it in lets an unseen impression win the as-of match over a seen one.
- Stack both frames into one long frame with a ts column and an is_stream flag, then sort by (profile_id, content_version_id, ts, is_stream, negative slate_position) so that at an identical timestamp impressions sort before the stream, and among tied impressions the smallest slate_position sorts last.
- Forward-fill the candidate impression_id and rendered_at with groupby(['profile_id','content_version_id']).ffill(). The grouping is what stops the fill leaking across profiles; a global ffill on a sorted frame is the usual wrong answer.
- Apply the 30-minute window as a post-filter on the filled candidate, and let a stream that fails it go unattributed rather than falling back to an older row. Every candidate precedes the stream, so the gap grows monotonically as you walk backwards: if the nearest in-view impression is more than 1800 seconds old, no earlier one is inside the window either, and null is the correct answer. The window cannot be pushed onto the impressions frame beforehand in any case, because "within 30 minutes" is defined against a particular stream's started_at and one impression is a candidate for many streams. Dropping impressions rendered more than 30 minutes before the earliest stream is a safe prune, but it is an optimisation, not the rule.
- Slice the stream rows back out and compute the attributed share by surface, stating that unattributed streams (resume, direct link, radio seed) are not a surface and belong in their own bucket.
Worked solution 30 min
- imp = impressions[impressions.viewport_visible_ms > 0]; build an impression side with ts = rendered_at, is_stream = 0 and a sort helper neg_pos = -slate_position, and a stream side with ts = started_at, is_stream = 1, neg_pos = 0.
- Concatenate both, then sort_values(['profile_id','content_version_id','ts','is_stream','neg_pos']).
- Add cand_id and cand_ts columns carrying impression_id and rendered_at on impression rows and NaN on stream rows, then groupby(['profile_id','content_version_id'])[['cand_id','cand_ts']].ffill().
- Slice to is_stream == 1, compute gap = (ts - cand_ts) in seconds, and set cand_id to NA where the gap is null or above 1800.
- Merge cand_id back onto streams by stream_id, then group the attributed rows by the winning impression's surface for the share.
Follow-up
- The same item was rendered in two slates inside the window. Your tie-break picks one. What does that do to per-surface credit, and what would a fractional rule change?
- How would you validate this against the impression_id already carried on fct_stream, and what would a systematic disagreement tell you?
- At 2 million impressions and 500 thousand streams this fits in memory. What changes at 200 million?
Can you explain the difference between supervised and unsupervised lea…
Can you explain the difference between supervised and unsupervised learning?
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which table is the grain you start from, and join outward from it.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- How does the query change if the join becomes one-to-many?
- What breaks if events arrive late or out of order?
Write a SQL query to extract specific data from a database.
Write a SQL query to extract specific data from a database.
Approach
- 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.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- How does the query change if the join becomes one-to-many?
- What breaks if events arrive late or out of order?
Sessionise playback into listening sessions with an idle gap
fct_stream has profile_id, stream_id, content_version_id, started_at, ended_at (nullable when the client stopped reporting) and played_seconds. Collapse consecutive streams by the same profile into listening sessions: a new session begins when a stream starts more than 30 minutes after the latest end reached by any of that profile's earlier streams. One profile can play on two devices at once, so streams overlap and 'the previous stream' is not well defined — the idle gap is measured against the running maximum end, not against the row immediately before. Treat a NULL ended_at as started_at plus played_seconds. Return one row per session with profile_id, session_start, session_end, stream_count, distinct content_version_id count and total played_seconds. Use window functions; a self-join on fct_stream is not an acceptable answer.
Approach
- In a first CTE compute effective_end = COALESCE(ended_at, started_at + played_seconds * INTERVAL '1 second'). This both fills the abandoned-client case and bounds it, since played_seconds is advancing playback only and cannot run past the true end.
- Take prev_max_end = MAX(effective_end) OVER (PARTITION BY profile_id ORDER BY started_at, stream_id ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING), and flag is_new_session = (prev_max_end IS NULL OR started_at > prev_max_end + INTERVAL '30 minutes'). LAG(effective_end) is the reflex here and it is wrong under concurrent devices: with A 10:00-12:00, B 10:05-10:10 and C starting 10:45, LAG hands C the end of B at 10:10, so C opens a new session 35 minutes into a session still running until 12:00. The NULL branch is not defensive noise either: the first row of every profile has no preceding frame and would otherwise fall outside any session.
- Number the sessions with SUM(is_new_session::int) OVER (PARTITION BY profile_id ORDER BY started_at, stream_id ROWS UNBOUNDED PRECEDING). A running count of boundaries is the gaps-and-islands device. Specify ROWS rather than letting the default RANGE frame apply, because RANGE lumps peer rows that share a started_at into one frame position — the same reason the ORDER BY carries stream_id as a tie-break.
- Group by (profile_id, session_number) and aggregate: MIN(started_at), MAX(effective_end), COUNT(*), COUNT(DISTINCT content_version_id), SUM(played_seconds).
- The running maximum makes the sessions disjoint in wall-clock time, but it does not make SUM(played_seconds) an elapsed-time figure: two devices playing at once contribute overlapping seconds, so the total can exceed session_end minus session_start. Report it as consumption and say which of the two the requester asked for.
Worked solution 30 min
- Materialise the effective_end CTE and check for rows where effective_end < started_at, which means negative played_seconds or a clock-skewed ended_at and must be quarantined before anything else.
- Add the running MAX and the boundary flag; verify that exactly one row per profile has prev_max_end IS NULL and that it is flagged as a session start.
- Add the running SUM to number sessions; confirm the numbers are dense and begin at 1 within each profile.
- Aggregate and reconcile: SUM(stream_count) across all sessions must equal the input row count.
- Run the overlap fixture — one profile with A 10:00-12:00, B 10:05-10:10 and C at 10:45 — and confirm it collapses to one session. With LAG(effective_end) in place of the running MAX it splits into two, and the second session's start, 10:45, sits inside the first session's span.
Follow-up
- Two devices on one profile play simultaneously. Is that one session or two, and what changes in the query for each answer?
- Re-run at a 10-minute and a 60-minute idle gap. How far does session count move, and which number do you publish and defend?
- Can SUM(played_seconds) for a session ever exceed session_end minus session_start, and what does it mean when it does?
How would you analyze customer churn for a subscription service?
How would you analyze customer churn for a subscription service?
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
- 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?
Describe your approach to anomaly detection in a dataset.
Describe your approach to anomaly detection in a dataset.
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- Name one primary metric, then the guardrail that stops it being gamed.
- State what result would change your recommendation, so the answer is falsifiable.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
What metrics would you use to evaluate the success of a model deployed…
What metrics would you use to evaluate the success of a model deployed in production?
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- 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?
How do you prioritize your work when managing multiple projects?
How do you prioritize your work when managing multiple projects?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Fix the population and the time window before naming any metric.
- 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?
Publish a per-person metric when no table holds a person
Leadership asks for streams per user per week on a weekly dashboard. You have dim_account (account_id, plan_tier, max_profiles), dim_profile (profile_id, account_id, profile_type, personalisation_opt_out, last_active_ts) and fct_stream (profile_id, account_id, device_type, started_at, played_seconds, is_qualified). Neither table holds a person. Deliverable: state the unit you will publish, define the metric fully, name what that unit actually aggregates, and give one diagnostic that tells the dashboard's readers how much of the data has no identifiable person behind it.
Approach
- Establish that both available units are wrong in different directions: an account is a household so it aggregates several people, and a profile is not reliably one person either because profiles are frequently not switched and shared-screen, car and speaker playback lands on whatever profile was last active or on a default.
- Pick the account as the published unit and justify it by what the number will be used for: billing, churn and revenue denominators are all per account, so an account-grained engagement metric joins to every other number on the dashboard without an impedance mismatch, while a profile-grained one silently changes the denominator between panels.
- Define it fully: qualified hours per active account-week, numerator sum(played_seconds)/3600 over is_qualified rows, denominator distinct (account_id, ISO week) pairs with at least one qualified stream, is_test_account excluded, window the four most recent complete ISO weeks, with the account-level two-distinct-date rule used for the active-account count.
- Label what the unit aggregates directly on the dashboard, not in documentation: the figure is hours per subscribing household per week, and family and duo plan_tier accounts read higher for composition reasons and are not more engaged people.
- Give the diagnostic that makes the ambiguity visible: share of qualified hours on profile_type = 'default_unnamed' plus the share on device_type in ('smart_speaker','car','smart_tv'), published beside the headline, since that is the fraction of consumption whose person is genuinely unknown and it tells a reader how much interpretive weight the number bears.
- Rule out the false fix explicitly: never divide account hours by max_profiles, because max_profiles is a plan entitlement that does not vary with household size and dividing by it manufactures a per-person figure that is a deterministic function of the plan.
Worked solution 25 min
- Write one sentence each for what dim_account and dim_profile actually identify, and state the two mechanisms that break the profile-equals-person assumption.
- Write the chosen metric in full, including numerator, denominator, window, exclusions and the two-distinct-date active rule, and state the reason the account grain matches the other dashboard panels.
- Write the unidentified-hours diagnostic as a query shape: share of sum(played_seconds) where profile_type = 'default_unnamed' or device_type in ('smart_speaker','car','smart_tv'), over total qualified played_seconds, by week.
- Write the label that sits under the headline number naming the household interpretation, in the words a non-analyst reader would need.
- State the prohibition on max_profiles division and the one-line reason, so the next person to ask for per-person numbers gets an answer rather than a repeat.
Follow-up
- Personalisation claims a win measured at the profile level. What do you need to know before you believe it is a person-level effect and not a device or context effect?
- A cut of the dashboard by plan_tier shows family accounts at three times individual accounts on hours. Write the sentence you would put under that chart.
- If you could add one column to dim_profile to make this less ambiguous, what would it be and what would it still fail to tell you?
Consumption fell the week a new ranker shipped
Qualified hours fell 4% in the seven days after a home-row ranker shipped to 100%, and a rollback is being demanded for tomorrow. You have fct_stream (played_seconds, is_qualified, content_version_id, start_source, started_at, play_territory), fct_impression (ranker_version, content_version_id, rendered_at), dim_content_version (content_id, parent_id, rights_expires_at, available_territories, catalogue_added_ts, is_active_version), and no holdout — the rollout went from 5% straight to 100%. Determine whether the ranker caused the fall. Deliverable: a defensible causal read, or an explicit statement of what cannot be concluded and what you would run instead.
Approach
- Check supply before demand. Sum the prior period's qualified hours on content_version_ids whose rights_expires_at falls inside the window, whose is_active_version flipped, or whose available_territories lost a territory. If titles carrying several points of hours left the catalogue that Monday, the fall is partly accounted for before the ranker is even discussed.
- Separate the mechanically unavailable from the merely deranked. Hours lost on items still available but no longer surfaced belong to the ranker; hours lost on items that cannot legally be served do not, and no ranking change recovers them. Only the first category is evidence for a rollback.
- Recover a comparison the rollout destroyed. With no holdout, use the 5% phase as a pre-period and territories unaffected by the expiry as a control, running difference-in-differences with the expiry as the treatment. State the parallel-trends assumption out loud and test it on the four pre-window weeks; abandon the estimator if it fails rather than reporting it with a caveat.
- Measure displacement instead of gross hours. For accounts whose usual items left, did total hours fall or did they substitute within the catalogue? Fully substituted removal costs nothing in hours and shows up only in the affected rights holder's payout, which is a different problem with a different owner.
- Ask separately what the ranker did to exposure: impression share by content_id under each ranker_version, and top-1% exposure concentration. A ranker that concentrates exposure can depress hours through a mechanism nobody has named yet, and this is what distinguishes that story from the rights story.
- Refuse the causal claim the rollout cannot support. Say plainly that a 5%-to-100% ramp across a confounded week yields no unbiased effect estimate, and propose the switchback or re-randomised holdout that would, with its cost stated.
Follow-up
- Design the switchback: slice length, burn-in long enough to clear carryover on a personalised surface, and how the standard error handles correlation between adjacent slices.
- If the rollback is ordered regardless of your analysis, what do you measure during the rollback to get a second, independent read?
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 ↗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 ↗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.
Sometimes the honest read is that the initiative did not work, and the person who commissioned the analysis was hoping otherwise. Interviewers want to know whether you softened it. Prepare the case where you delivered an unwelcome result, how you presented the uncertainty without hiding behind it, and what the team did next.
Tell me about a time you had to work with a difficult team member. How…
Tell me about a time you had to work with a difficult team member. How did you handle the situation?
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?
- What would you do differently if you ran that project again?
Scope a one-line request about home row performance
A director messages you: "Is the new home row working?" Nothing else. A new ranker_version has been serving a fraction of profiles for eleven days. You have fct_impression (surface, slate_position, ranker_version, is_exploration_slot, logging_propensity, experiment_assignment_id, was_clicked) and fct_stream (impression_id, start_source, is_qualified, played_seconds). You get a fifteen-minute call before they go into a rollout meeting. Deliverable: the three questions you ask before writing any SQL, the single primary metric you commit to with its guardrail, and the questions you tell them this data cannot answer.
Approach
- The probe is whether you convert a vague request into a decision before producing a number. Ask what happens at each answer — rollback, widen, iterate — because a question whose answer changes nothing is a report request and should be scoped as one.
- Pin the unit of analysis out loud. fct_impression is at (profile, slate, slot) grain and experiment_assignment_id is per assignment, so the comparison must be aggregated to the assignment unit first; comparing impression-level rates lets a change in slate length move the metric on its own.
- Commit to one primary metric from the tree — qualified hours per active account-week for assigned accounts — and name the guardrail pair explicitly: share of qualified streams with start_source = 'autoplay_continuation', and median completion_ratio within content_type. A ranker can lift qualified stream counts by queueing short items that clear the 30-second threshold, and the guardrail is the only thing that catches it.
- State the refusals with structural reasons, not time reasons: eleven days gives no matured cohort, so month-6 retention and net revenue per active account-month are unanswerable; and logging_propensity is populated only where is_exploration_slot = true, so the positivity condition for an off-policy estimate fails outside those slots.
- Write the scope back in one paragraph — decision, metric, guardrail, the date the read becomes valid — and get it agreed in the thread before querying, so the number that arrives is the number that was asked for.
Follow-up
- They reply "just give me click-through by slate position." What do you say, and what would that number actually tell them?
- The eleven days include a weekend and a large release landing on day six. Does that change the metric you commit to, or only the read date?
- What would have to be true for you to be willing to answer the retention question from this experiment?
Walk through an analysis you shipped that was wrong
Describe a case where you delivered a result that was later shown to be wrong, and it had already been acted on. Cover how the error surfaced, whether you or someone else found it, what the wrong number caused, and what you changed afterwards. Prepare an example whose root cause was a definition, a denominator or a join — not a transcription slip. The interviewer will push on the mechanism, not the apology. Deliverable: a four-minute account that ends with a specific control now running in a pipeline.
Approach
- The probe is whether your account has a mechanism in it. Choose an error that generalises — a denominator that silently changed population, a join that fanned rows, a metric partitioned on event_date while offline playback arrived days late and landed in the wrong partition — rather than one that only teaches you to check your typing.
- State the blast radius factually and early: which decision was taken, how long the number stood, what it cost. A candidate who softens this is answering a different and easier question, and the interviewer can hear the substitution.
- Explain how it surfaced without adjusting who found it. The generalisable detail is why your own checks did not catch it, which is a statement about your checks rather than about your luck.
- Name the control you added and where it now lives: a row-count assertion after the fan-out join, a reconciliation that recomputes a closed day after late-arriving offline playback and alerts above a threshold, a denominator assertion inside the query. A fix that lives in a pipeline is different in kind from a resolution to be more careful.
- Close with whether the control has fired since, or how you tested that it would. That single sentence is what separates a fix from an intention, and interviewers ask for it when candidates do not offer it.
Follow-up
- Why didn't your own review catch it? Be specific about what you did check.
- What class of error would that control still not catch, and what would you add next?
- Have you found an error in someone else's published analysis since? How did you raise it?
- 01
Tell me about a time you had to work with a difficult team member. How did you handle the situation?
- 02
A director messages you: "Is the new home row working?" Nothing else. A new ranker_version has been serving a fraction of profiles for eleven days. You have fct_impression (surface, slate_position, ranker_version, is_exploration_slot, logging_propensity, experiment_assignment_id, was_clicked) and fct_stream (impression_id, start_source, is_qualified, played_seconds). You get a fifteen-minute call before they go into a rollout meeting. Deliverable: the three questions you ask before writing any SQL, the single primary metric you commit to with its guardrail, and the questions you tell them this data cannot answer.
- 03
Describe a case where you delivered a result that was later shown to be wrong, and it had already been acted on. Cover how the error surfaced, whether you or someone else found it, what the wrong number caused, and what you changed afterwards. Prepare an example whose root cause was a definition, a denominator or a join — not a transcription slip. The interviewer will push on the mechanism, not the apology. Deliverable: a four-minute account that ends with a specific control now running in a pipeline.
Is this an official Uptake interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Uptake. Rounds and questions reflect what candidates have reported, not a process Uptake has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗What is the typical interview difficulty and preparation time?
The interview process for a Data Scientist role at Uptake can be considered moderate to difficult. Candidates typically prepare for several weeks, focusing on both technical and behavioral aspects to ensure a well-rounded approach.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates demonstrate a strong understanding of data science principles, effective problem-solving skills, and the ability to communicate complex ideas clearly. They also align closely with Uptake's values and culture.
PracHub interview research ↗How does the culture and working style at Uptake look?
Uptake fosters a collaborative and innovative environment where team members are encouraged to share ideas and continuously improve. The company values data-driven decision-making and strives for excellence in all projects.
PracHub interview research ↗What is the typical timeline from the initial screen to an offer?
The timeline can vary, but candidates generally receive feedback within a few weeks after the final interview. The entire process from screening to offer may take up to one month.
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
Explore related engineering environments and compare the preparation focus.
Zoom Video Communications Data Scientist Interview Questions & Guide 2026
Communication practice for Zoom Video Communications data scientist candidates: turn an analysis into a recommendation, name the uncertainty honestly, and answer the follow-up a skeptical stakeholder will raise.
Warner Bros. Discovery Data Scientist Interview Questions & Guide 2026
Statistics practice aimed at Warner Bros. Discovery candidates: conditional probability, sampling distributions, confidence intervals people routinely misread, and saying out loud which assumption a test depends on.
Yahoo Data Scientist Interview Questions & Guide 2026
Statistics practice aimed at Yahoo candidates: conditional probability, sampling distributions, confidence intervals people routinely misread, and saying out loud which assumption a test depends on.
Warner Bros. Data Scientist Interview Questions & Guide 2026
Take-home style practice for data scientists preparing for Warner Bros.: a messy dataset, a fixed time budget, and a written analysis, reviewed for whether it states its assumptions and whether a reader could reproduce your numbers.