As a Data Scientist at Jellyfish, you are at the heart of transforming complex engineering data into actionable strategic insights. Your work directly influences how organizations understand their engineering velocity, resource allocation, and operational efficiency. By analyzing large-scale development data, you help bridge the gap between technical output and business outcomes.
This role requires a unique blend of technical rigor and business intuition. You will not only build models or conduct analyses; you will be expected to translate these findings into clear, persuasive narratives for internal stakeholders and, at times, customers. The work is high-impact, as your contributions help define the metrics that drive modern software engineering management.
Preparation focus
editorialNo round sequence has been reported for this company, so work the categories below and confirm the format with your recruiter.
What to demonstrate
- Breadth across SQL, experimentation and product reasoning
- Ability to state assumptions before choosing a method
How to prepare
- Drill the practice exercises below and time yourself
- Prepare three quantified stories about decisions you drove
PracHub editorial advice for the preparation topics above.
Watching an experiment daily and stopping when it crosses significance
A fixed-sample test controls type I error at one pre-declared look. Checking repeatedly and stopping at the first p < 0.05 inflates the false positive rate to roughly 0.15 to 0.20 for ten looks, and it rises further with more frequent checks, because the p-value takes a random walk that will eventually dip below the threshold under the null. The usual defences are a fixed horizon declared before launch, group-sequential boundaries such as O'Brien-Fleming that spend alpha across a planned number of looks, or always-valid confidence sequences that are correct under continuous monitoring. Compounding it, the effect size reported conditional on having crossed the threshold is biased away from zero, and the bias is larger the lower the power was, so an underpowered test that 'won' typically overstates the lift it found.
Slicing a flat experiment until a segment reaches significance
Testing one metric across twenty segments at a nominal 5% level produces a significant result about two thirds of the time when nothing is happening anywhere, and the segment that surfaces is by construction the one with the most favourable noise. The reported effect in that slice is then badly overstated, because selection on significance conditions the estimate on being large. What makes it dangerous rather than merely wrong is that a post-hoc segment always has a plausible story attached, so it survives the meeting. The controls are declaring the small number of segments of interest before launch, correcting across the ones tested, and treating anything discovered afterwards as a hypothesis that needs its own adequately-powered test rather than a finding.
Comparing periods without accounting for seasonality or day-of-week
Compare whole weeks against whole weeks and check whether the same swing appeared in prior cycles or prior years before attributing it to anything you changed. Weekday and weekend populations often differ enough that a Tuesday-to-Saturday comparison is meaningless.
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.
If you have a bag of jelly beans with a large number of two different …
If you have a bag of jelly beans with a large number of two different colors, what is the minimum you need to pick out to guarantee that you have at least 2 of the same color?
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Write down the assumption the method needs before you use the method.
- Translate the result into the decision it informs, in one plain sentence.
Follow-up
- What sample size would you need to detect an effect half this size?
- Which assumption here is most likely to be violated in practice?
Can you explain the logic behind the previous answer for the general c…
Can you explain the logic behind the previous answer for the general case of N colors?
Approach
- Write down the assumption the method needs before you use the method.
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Translate the result into the decision it informs, in one plain sentence.
Follow-up
- Which assumption here is most likely to be violated in practice?
- What sample size would you need to detect an effect half this size?
Audit a one-day event extract for structural defects
You receive a one-day extract of fct_event as a DataFrame with event_id, occurred_at_utc, received_at_utc, visitor_id, user_id, account_id, event_name, is_bot_flagged and surface. Write a function returning one row per data-quality rule with the rule name, the failing row count and the failing share of the extract. Cover at minimum: duplicate event_id, received_at_utc earlier than occurred_at_utc, occurred_at_utc later than the extract's maximum received_at_utc, account_id present while user_id is NULL, and rows whose occurred_at date differs from their received_at date. Do not drop rows; report only.
Approach
- Compute the extract's own reference clock first: max(received_at_utc). Wall-clock now() is wrong here because the extract may be replayed days later, which would turn every row into a future-dated failure.
- Express each rule as a boolean Series over the same index so the checks compose, then aggregate with .sum() and divide by len(df). Building a list of (name, mask) pairs keeps the rule set extensible and keeps one code path for counting.
- For the duplicate rule, decide and state the convention: df.duplicated('event_id', keep=False).sum() counts every member of a duplicated group, df.duplicated('event_id').sum() counts only the surplus copies. Either is defensible; an unstated choice is not. The rest of this item assumes keep=False.
- Treat received_at < occurred_at as clock skew, not corruption: occurred_at is client-supplied. Separate it from the date-mismatch rule, which is the one that actually breaks a daily metric keyed on occurred_at.
- Know which rules imply which before you read the counts. A row whose occurred_at exceeds max(received_at_utc) has its own received_at no later than that maximum, so it is necessarily a clock-skew row as well: the future-dated mask is a subset of the skew mask, always. Neither is a subset of the date-mismatch mask, because skew of a few minutes inside one UTC date mismatches nothing.
- Return a tidy DataFrame sorted by failing_share descending, and add a boolean column saying whether the rule should block publication, so the output is a decision rather than a list of numbers.
Worked solution 20 min
- Parse both timestamp columns with utc=True and assert the dtype, since a silently-object column makes every comparison string-wise and wrong.
- Set ref = df['received_at_utc'].max() and build the five masks against it.
- Assemble results as pd.DataFrame(rows) with columns rule, failing_rows, failing_share, blocks_publication.
- Keep the masks addressable (a dict of name to Series) rather than only their sums, so the overlap between rules can be asserted rather than assumed.
- Verify the function is pure: assert the input frame's shape is unchanged after the call.
Follow-up
- The date-mismatch count is 2.1 percent on this extract. What late-arrival rule would you write for a daily metric, and how many days would you hold the number open?
- Duplicate event_id values appear only on the 'core_action_completed' event. What upstream cause would you check before deduplicating?
- Which of these rules should fire an alert at the pipeline, and which should only appear in a weekly review?
Can you walk me through your process for cleaning and preparing a mess…
Can you walk me through your process for cleaning and preparing a messy dataset?
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
Rebuild sessions from raw events with a thirty-minute gap
From fct_event (event_id, visitor_id, occurred_at_utc) alone, rebuild sessions: a new session begins when the gap from that visitor's previous event exceeds 30 minutes, and every session is force-closed at UTC midnight so none spans two calendar dates. Return one row per session with visitor_id, session_start, session_end, event_count and session_date. Do not read fct_session; the point is to reproduce it. Assume duplicate occurred_at_utc values exist for the same visitor.
Approach
- Get the previous timestamp per visitor with LAG(occurred_at_utc) OVER (PARTITION BY visitor_id ORDER BY occurred_at_utc, event_id). The event_id tiebreaker is required, not stylistic: with duplicate timestamps an unstable ordering makes the boundary flags non-deterministic between runs.
- Set a boundary flag when prev IS NULL, or occurred_at_utc - prev > interval '30 minutes', or occurred_at_utc::date <> prev::date. The third disjunct is the midnight rule, expressed as a date change rather than a clock comparison so it holds across any gap length.
- Number the islands with SUM(flag::int) OVER (PARTITION BY visitor_id ORDER BY occurred_at_utc, event_id ROWS UNBOUNDED PRECEDING). Because event_id is unique the ordering is total, so no two rows are peers and RANGE UNBOUNDED PRECEDING would compute exactly the same numbers here. Write ROWS anyway: it is the half of the guard that survives someone later simplifying the ORDER BY back to occurred_at_utc alone, at which point the default RANGE frame gives every row sharing a timestamp one shared running total.
- GROUP BY visitor_id and the island number, then MIN(occurred_at_utc) AS session_start, MAX(...) AS session_end, COUNT(*) AS event_count, session_start::date AS session_date.
- Reconcile against fct_session on one sample day. The counts should agree except for server-emitted events carrying no client session, so a systematic difference beyond those is a bug in the gap rule or in the ordering.
Worked solution 30 min
- Pick one high-volume visitor and dump their ordered event timestamps for a day to trace by hand.
- Add LAG with the tiebreaker and eyeball the computed gaps.
- Add the three-part boundary flag and confirm the first event of each date is flagged.
- Add the running SUM with an explicit ROWS frame, then group and aggregate.
- Compare total event_count against the raw input count, and compare session counts to fct_session for the sample day.
Follow-up
- Why 30 minutes? What does a 5-minute rule do to sessions-per-visitor and to any per-session conversion rate?
- The same person uses phone then laptop. Two visitor_ids, two sessions. What breaks if you sessionise on user_id instead?
- The midnight rule splits an overnight session. Which metrics does that bias, and in which direction?
How do you optimize a Python script that is processing large-scale log…
How do you optimize a Python script that is processing large-scale logs?
Approach
- 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.
- Restate the decision this analysis has to support, and who acts on the answer.
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 an experiment to measure the impact of a new feat…
How would you design an experiment to measure the impact of a new feature?
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
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
Size an activation test before anyone writes code
A team wants to test a new onboarding checklist against the seven-day activation rate: users with is_core_action = TRUE events on at least two distinct UTC dates inside the first seven days, counted from dim_user.account_created_at_utc with is_internal = FALSE. Baseline is 22%. Weekly non-internal signups are 9,000, split evenly between two arms. State the minimum detectable effect after two weeks of enrolment, the enrolment needed to detect a 1.5 percentage point absolute lift, and the earliest date a readout would be honest.
Approach
- Use the two-proportion sizing shortcut n = 16 p(1-p) / d^2 per arm, where d is the absolute lift. The 16 is 2(1.96 + 0.84)^2 = 15.7 rounded, i.e. two-sided alpha 0.05, 80% power, equal arms.
- Invert it for the MDE at fixed n: d = sqrt(16 p(1-p) / n). Two weeks gives 9,000 per arm, so d = sqrt(2.746 / 9000) = 0.0175, which is 1.75 percentage points absolute or 7.9% relative on a 22% base.
- Run it forward for d = 0.015: n = 2.746 / 0.000225 = 12,202 per arm, 24,404 total, about 2.7 weeks of enrolment. Round up to three whole weeks so the enrolment window contains no partial week, since signups are not uniform across weekdays.
- Add the metric's own lag rather than reporting the enrolment end date. Activation is unobservable until seven days after the last enrolled signup, and the definition publishes on an eight-day lag, so the readout lands 29 days after launch.
- Close by testing whether the ask is realistic. If the team's honest prior is a 0.5pp lift, the requirement is about 110,000 per arm and roughly 24 weeks of enrolment, and saying so before launch is the deliverable.
Worked solution 20 min
- Compute p(1-p) = 0.22 x 0.78 = 0.1716 and 16 x 0.1716 = 2.746.
- MDE at two weeks: n = 4,500 per arm per week x 2 = 9,000; d = sqrt(2.746 / 9000) = 0.0175.
- Sample for d = 0.015: n = 2.746 / 0.000225 = 12,202 per arm, which is 2.71 weeks of enrolment, so enrol three whole weeks.
- Readout date = 21 days of enrolment + 8 days of follow-up and publication lag = 29 days from launch.
- State the boundary of what this quarter can answer: below roughly 0.7pp the requirement passes 56,000 per arm and more than a full quarter of enrolment.
Follow-up
- The team also wants trial-to-paid read from the same test. What does a second primary metric do to your alpha, and what would you actually do about it?
- Signups are heavier on weekdays. What goes wrong if enrolment runs 17 days instead of 14?
- The activation definition uses a two-distinct-days threshold. If that were loosened to one day, what happens to the baseline rate and to the sample you need?
Pooled signup conversion fell while every segment rose
Weekly visit-to-signup conversion, counted on distinct fct_session.visitor_id with is_bot_flagged = TRUE and consent_state = 'denied' sessions excluded, fell from 4.4% to 3.9% week over week. Split by device_type and referrer_channel, all twelve cells are flat or up. A paid_social campaign launched on Monday. Using fct_session and fct_event, quantify how much of the 0.5-point fall is mix and how much is within-segment rate, then state what you would tell the growth lead.
Approach
- Write the pooled rate explicitly as the sum over segments of weight times segment rate, and materialise both weeks' weights and rates into one table. Until that table exists there is nothing to decompose, only opinions.
- Compute three quantities and report all three: the rate effect holding the prior week's weights fixed, the mix effect holding the prior week's rates fixed, and the interaction residual. Reporting only the first two hides a term that can be material when both weights and rates move a lot.
- Rank segments by their individual mix contribution, computed as the change in that segment's weight multiplied by its prior-period rate. This is what lets you say one cell caused the move rather than gesturing at the campaign.
- Verify the new traffic is human and countable before accepting the mix story: check is_bot_flagged coverage on the new channel, the distribution of duration_seconds and event_count for its sessions, and whether its consent_state profile differs from the rest.
- Deliver the conclusion as a definition change rather than a diagnosis: a pooled rate over a mix that moves is not comparable week over week, so the recurring report should carry per-channel rates plus absolute signups, with the pooled figure demoted or dropped.
Follow-up
- Paid social converts at roughly a quarter of organic but absolute signups rose. Is the campaign working, and what would you need to answer that properly?
- Would you reach the same conclusion if the campaign had moved the mix by two points instead of sixteen? Where is your threshold and why?
- How would you present this to someone who has been watching the pooled number in a weekly meeting for a year?
For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Metric anatomy
- For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
- For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
- Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.
Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Diagnosing a drop without guessing
- Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
- List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
- Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.
Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.
Practice prompt ↗Practice prompt ↗03Should we build it
- Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
- Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
- Write the counter-metric that would make you kill the feature even if it wins on the primary metric.
Deliverable: A one-page product memo ending in a decision rather than a list of considerations.
Practice prompt ↗Practice prompt ↗04The places aggregate numbers lie
- Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
- Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
- Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.
Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Technical maintenance, aimed at metrics
- Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
- Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
- Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.
Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.
Practice prompt ↗Practice prompt ↗06Turning engineering work into data science stories
- Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
- For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
- Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.
Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.
Practice prompt ↗07Mock case and gap list
- Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
- Listen back and mark every moment you proposed a solution before the success metric existed.
- Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.
Deliverable: A recorded case plus a rewritten opening 90 seconds.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.
Disagree with a product manager's roadmap claim using data
A product manager proposes building a feature on the argument that accounts connecting an integration in week one retain three times better at week four. The figure is correctly computed from dim_user and fct_event, and it has already been shown to leadership. You have one scheduled 1:1 before the roadmap locks. Deliver the specific analysis you would run to test whether the relationship is causal, the result that would change your own mind, and how you open the conversation so that the PM is not put in the position of defending the number in public.
Approach
- Recognise what is being probed: whether you can separate a number being right from an inference being wrong, and do it without costing the PM face. The generic answer recites that correlation is not causation; the strong one names the specific confound and proposes the cheapest design that could distinguish the explanations.
- State the alternative concretely. Accounts that connect an integration in week one are accounts that already have a workflow and a technical owner, so week-one intent plausibly drives both the connection and week-four retention. The selection is on intent, which no amount of post-hoc adjustment observes.
- Order the discriminating analyses by cost. First, condition on pre-connection activity by comparing retention within strata of week-one core-action count, which removes the crude version of the confound but not unobserved intent. Second, look for variation in integration availability that was unrelated to intent, such as a staggered release or an outage window. Third, an encouragement design that randomises a prompt to connect and reads the intent-to-treat effect on week-four retention, which is the only version that identifies an effect.
- Run the timing check, because it is nearly free and it is the most persuasive single piece of evidence. If the retention advantage among connectors is already visible before any of them connected, the causal story is largely finished.
- Pre-commit to what would change your mind and say it before you show anything: if the gap survives stratification and the encouragement arm moves week-four retention at all, the feature has a case and you will say so.
- Open the 1:1 by agreeing with the true part, that the correlation is real and worth chasing, then ask what effect size the roadmap plan assumes. That makes the size of the claim the topic instead of its authorship.
Follow-up
- The encouragement test needs six weeks and the roadmap locks in two. What do you recommend in the interim?
- Stratifying on week-one activity closes half the gap. What do you conclude, and what do you still not know?
- How would you word this in the roadmap document so the PM's original number is reframed rather than deleted?
Quantify your own impact without claiming the topline you touched
You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.
Approach
- Recognise what is being probed: whether you apply to your own work the causal standard you would apply to somebody else's roadmap claim. Nearly everyone who would reject 'accounts that do Y retain better' will write 'I drove a 14% increase' without noticing it is the same error with a friendlier subject.
- Sort the work by the kind of evidence it can carry. The shipped experiment is the only item with a randomised estimate, so it is the only one where an effect size is defensible, and you claim the interval rather than the point estimate.
- Claim the three flat experiments as decisions prevented and price them. Features not built, or built differently, on evidence, with the engineering weeks reallocated as the number somebody else can verify. A defensible null is a delivered decision and should be written as one.
- Claim the definition fix as correctness, not as improvement. The old figure was overstated by a specific percentage and appeared in a specific set of recurring documents; the impact is the change it produced in the forecast built on top of it, not a change in churn itself.
- Claim the dashboard on usage and displacement: distinct weekly users of it, and the ad-hoc request count for six months before against six months after. If the request log does not exist, record the claim as unverified rather than estimating it upward.
- Disclaim the 14% explicitly and once. State that it cannot be separated from seasonality, other teams' launches and a pricing change, and bound your own contribution from above using the shipped experiment's interval converted into headline units.
Follow-up
- Your shipped experiment's interval was +0.2pp to +1.4pp on activation. How much of the 14% can that account for, and how do you say so without undercutting yourself?
- A peer in the same cycle claims the full 14%. What, if anything, do you do about it?
- If you could only keep two of your three claims, which do you drop, and why that one?
Handle a request for numbers supporting a decision already made
A senior leader has already decided to sunset a plan tier and asks you for the analysis showing it is the right call. Accounts on that tier carry 6% of MRR at constant FX and have the highest licensed-seat utilisation in the book. The leader's support matters to your next review cycle, and the decision is being presented in four days. Deliver what you produce, what you decline to produce, and the exact sentence you will say in the meeting where the number appears on a slide.
Approach
- Recognise what is being probed: whether you can find the legitimate request inside an illegitimate framing instead of either complying or refusing on principle. The generic answer promises to push back; the strong one produces something genuinely useful and states its limits in the room, without ambushing anybody.
- Separate the decision from the justification. Sunsetting the tier may be correct for reasons the data does not hold, such as support cost, roadmap surface area or sales motion. What you decline is a one-sided document. What you produce is the case read both ways, which also happens to be more useful to the leader.
- Build the symmetric analysis: MRR at risk at constant FX, the share of affected accounts with a plausible migration path given seats_licensed and billing_term, the recovery rate assumed for that migration and where it came from, and the downside case in which high-utilisation accounts treat the sunset as a reason to re-evaluate the vendor entirely.
- Surface the inconvenient fact privately and early. The highest seat utilisation in the book is a retention signal, and the leader should hold it before the room does, so they can incorporate it rather than be caught by it.
- Agree the meeting sentence in advance with the leader, so that nobody is surprised. Something to the effect that the tier is 6% of MRR and its accounts are the most heavily used in the book, and that the case for sunsetting rests on cost and focus rather than on revenue. That is true, it supports the decision on its real grounds, and it stops the deck claiming the numbers endorse it.
- Decide your own line before you need it: what you will not put your name to, and that the route if asked anyway is your own manager rather than a confrontation in the meeting.
Follow-up
- The deck circulates with your analysis included and the downside case removed. What do you do, and by when?
- What changes if the honest analysis says the sunset is clearly the wrong call?
- How do you write the same memo when the leader is your skip-level and the meeting is tomorrow?
- 01
A product manager proposes building a feature on the argument that accounts connecting an integration in week one retain three times better at week four. The figure is correctly computed from dim_user and fct_event, and it has already been shown to leadership. You have one scheduled 1:1 before the roadmap locks. Deliver the specific analysis you would run to test whether the relationship is causal, the result that would change your own mind, and how you open the conversation so that the PM is not put in the position of defending the number in public.
- 02
You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.
- 03
A senior leader has already decided to sunset a plan tier and asks you for the analysis showing it is the right call. Accounts on that tier carry 6% of MRR at constant FX and have the highest licensed-seat utilisation in the book. The leader's support matters to your next review cycle, and the decision is being presented in four days. Deliver what you produce, what you decline to produce, and the exact sentence you will say in the meeting where the number appears on a slide.
Is this an official Jellyfish interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Jellyfish. Rounds and questions reflect what candidates have reported, not a process Jellyfish has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical assessments?
The difficulty varies, but they are generally focused on practical application rather than obscure trivia. Focus on writing clean, readable code and explaining your logic clearly.
PracHub interview research ↗What is the best way to stand out during the interview?
Show a genuine curiosity about how Jellyfish products impact their customers. Candidates who connect their technical skills to the company's business goals consistently perform better.
PracHub interview research ↗How long does the hiring process typically take?
It can range from a few weeks to a couple of months. While the process is efficient, be prepared for potential gaps between rounds.
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
Explore related engineering environments and compare the preparation focus.
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