The Data Scientist role at 6sense is pivotal in shaping the way the company leverages data to enhance its products and services. As a Data Scientist, you will be responsible for analyzing complex datasets to derive insights that inform strategic decisions, optimize user experiences, and drive business growth. This role is not just about crunching numbers; it involves understanding market trends, customer behaviors, and operational efficiencies, all of which are crucial for building innovative solutions.
At 6sense, Data Scientists work closely with cross-functional teams, including product management, engineering, and marketing, to solve challenging problems that impact the company’s bottom line. You will engage in projects that require high scalability and complexity, such as predictive modeling and machine learning applications, which are integral to the company’s data-driven approach. This dynamic environment offers an exciting opportunity to influence product development and strategic direction through data insights.
Candidates can expect to tackle significant challenges that contribute to the company's mission of revolutionizing sales and marketing through AI and data analytics. The role is critical, providing a unique chance to work at the intersection of technology and business, ensuring that your contributions have a real impact on both the company and its customers.
Recruiter Call
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
Take-Home Assessment
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
Technical Interview
reportedThis round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.
What to demonstrate
- Whether your row counts survive each join, and whether you notice on your own when they do not
- Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
- Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
- Reaching a defensible answer inside the window instead of a refined one after it
How to prepare
- Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
- Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
- Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither
Onsite Interview
reportedWhere a loop ends with a senior leader, that conversation is rarely another skills test. The technical signal already exists by then, so the questions tend to open up: what you would look at first, where a metric you have heard about could mislead, what you would push back on. The decision being made is scope, which in practice means level and how much you would be trusted to own unsupervised. Treating it as a formality is the usual mistake. An open question late in the day is still being scored, and a vague answer reads as someone who has not run anything themselves.
What to demonstrate
- Whether your view of the business has anything specific behind it, given that you are working only from what is public and are expected to say so
- Whether the scope of work you describe owning matches the scope of the role, instead of sitting a level below it
- Whether you can disagree with something concrete and stay useful about it, rather than agreeing with everything said in the room
- Whether your questions are ones only this person could answer, as opposed to ones the recruiter already covered
How to prepare
- Build one view you could defend for two minutes using only public information: what the funnel probably looks like, which metric likely drives decisions, and where that metric could mislead. Being wrong for a stated reason survives this round; having no view does not
- Write down the largest piece of work you have owned from question to decision, who else touched it, and what you decided alone, then check that it reads at the level you are interviewing for
- Prepare one thing you would want changed if you joined and phrase it as a question rather than a verdict, so it opens a conversation instead of closing one
PracHub editorial advice for the preparation topics above.
Counting on an identity key that changes underneath the metric
visitor_id is per browser and per device, and it resets on cookie clearance, private browsing and platform privacy changes, so the distinct-visitor count drifts upward for reasons unrelated to reach. Any rate with visitors in the denominator therefore decays over time even when behaviour is constant, and any rate with visitors in the numerator inflates. The stitching at signup makes it worse in both directions: a user who signed up on mobile and returns on desktop is two visitors and one user, while a shared device is one visitor and several users. Decide which key each metric is counted on, write it into the definition, and when comparing a period before and after a platform privacy change, expect a level shift in every visitor-keyed metric and do not attribute it to the product.
Reading a pooled rate that moved because the mix moved, not because any behaviour changed
A pooled conversion rate is a weighted average, and a shift in the weights can move it in the opposite direction to every one of its parts. A paid campaign that brings low-converting traffic drops overall signup conversion even if desktop, mobile web and app conversion each rose that week, which is Simpson's paradox and it is the single most common cause of an inexplicable dashboard move. The discipline is to decompose before explaining: recompute the rate holding last period's segment weights fixed, and compare that counterfactual to the actual, so the mix effect and the rate effect are separated numerically rather than argued about. Segment on the dimensions that actually reweight, which in this domain are almost always device_type, referrer_channel, country and new versus returning.
Solving silently instead of narrating the reasoning
Say which branch you are taking and why you chose it over the alternative, for example checking the denominator first because it changes what the comparison means. A correct answer that arrives with no visible path scores below a rigorous one that needed a hint.
Reaching for a model before the target metric exists
Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Implement a binary search algorithm.
Implement a binary search algorithm.
Approach
- Set a baseline first, so any model has something honest to beat.
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Check what information would not exist at prediction time, and exclude it.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
Discuss the importance of overfitting and how to avoid it.
Discuss the importance of overfitting and how to avoid it.
Approach
- Check what information would not exist at prediction time, and exclude it.
- Say how the offline result would be validated online before it is trusted.
- Set a baseline first, so any model has something honest to beat.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
Split a pooled conversion drop into rate and mix
You have weekly visit-to-signup counts by segment: a DataFrame with week, device_type, referrer_channel, visitors and signups. The pooled rate fell 0.84 percentage points between two consecutive weeks while several individual segments rose. Write a function that, for a caller-supplied list of segment columns, splits the pooled change into a rate effect, a mix effect and an interaction term that sum exactly to the observed change. Return those three scalars plus a per-segment contribution table sorted by absolute contribution, so the largest single driver can be named.
Approach
- State the algebra before coding: the pooled rate is r = sum over segments of w_s * r_s, with w_s the segment's share of the denominator. Then r1 - r0 decomposes exactly into sum(w_s0 * (r_s1 - r_s0)) for rate, sum((w_s1 - w_s0) * r_s0) for mix, and sum((w_s1 - w_s0) * (r_s1 - r_s0)) for interaction. The identity is per-segment, so it holds for any numbers you put in the four slots.
- Pivot both weeks onto a common segment index with an outer join so a segment that appeared or vanished is kept rather than dropped, then decide what rate to give a segment with no visitors in one of the weeks, and document the choice. The identity stays exact either way because the missing week's weight is 0, but the attribution does not. Filling the missing rate with 0 sends an appearing segment's entire w_s1 * r_s1 into the interaction term, since w_s0 = 0 makes both the rate term and the mix term (w_s1 - w_s0) * r_s0 identically zero; a vanishing segment then splits as -w_s0 * r_s0 in rate, -w_s0 * r_s0 in mix and +w_s0 * r_s0 in interaction.
- The convention used below instead imputes the missing week's rate as that week's pooled rate. A vanishing segment then lands wholly in mix at -w_s0 * r_s0, with rate and interaction cancelling; an appearing segment puts w_s1 * r_pooled0 in mix (volume arriving at the average rate) and only w_s1 * (r_s1 - r_pooled0) in interaction (its rate differing from that average). Impute by which week the segment is missing from, never by argument order, or the swap identities below stop holding.
- Guard the division where visitors is 0 so no NaN enters the vectors, because a single NaN poisons every sum. A segment with zero visitors in both weeks contributes exactly 0 and can be dropped; a segment missing from only one week does not contribute 0, and where its contribution lands is settled by the convention above, not by the guard.
- Compute the three components as vectors over segments, then sum. Keep the vectors, because the per-segment contribution table is what turns the decomposition into an explanation.
- Assert that the three components sum to the observed pooled change within floating-point tolerance. This identity is exact, so a mismatch means an implementation bug, not a modelling judgement.
Worked solution 25 min
- Aggregate to one row per (week, segment tuple) with summed visitors and signups, then split into w0 and w1 frames and align with an outer join, filling missing visitors and signups with 0.
- Compute w_s = visitors / visitors.sum() within each week, and r_s = signups / visitors only where visitors > 0. Where a week's visitors are 0, set that week's r_s to that week's pooled rate, the stated convention; never leave it NaN.
- rate_effect = (w0 * (r1 - r0)).sum(); mix_effect = ((w1 - w0) * r0).sum(); interaction = ((w1 - w0) * (r1 - r0)).sum().
- contribution = w0*(r1-r0) + (w1-w0)r0 + (w1-w0)(r1-r0) per segment, which reduces to w1r1 - w0r0; sort by abs and return the head.
- assert abs(rate + mix + interaction - (pooled1 - pooled0)) < 1e-12.
Follow-up
- The mix effect accounts for 0.71 of the 0.84 point drop, driven by paid_social volume. What is your recommendation, and what would change it?
- Why is a two-way split into a counterfactual rate and a residual also exact, and when would you prefer it to the three-way version?
- Segmenting on device and channel leaves a large interaction term. What does that tell you about the choice of segments?
How would you approach solving the traveling salesman problem?
How would you approach solving the traveling salesman problem?
Approach
- State the window function and its partition and ordering out loud before writing it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- 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 would you verify this result without re-running the same query?
Explain how you would use a map-reduce approach to process large datas…
Explain how you would use a map-reduce approach to process large datasets.
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.
- 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?
Read an experiment from first exposure, not assignment
fct_experiment_exposure holds experiment_id, unit_type, unit_id, variant, user_id, assigned_at_utc, first_exposed_at_utc, is_in_analysis_population and planned_end_utc. fct_event holds user_id, occurred_at_utc, is_core_action, and carries events up to a known data cut, :data_cut_utc. For one experiment randomised on unit_type = 'user', return per variant: exposed units, units with at least one core action in the seven days after that unit's own first exposure, the rate, and the variant share of exposed units. Only units whose seven-day window has fully elapsed as of the data cut belong in the readout. Units appearing under more than one variant are excluded from both arms and counted separately.
Approach
- Run the contamination pass as an aggregate, not a window: SELECT unit_id FROM fct_experiment_exposure WHERE experiment_id = :exp GROUP BY unit_id HAVING COUNT(DISTINCT variant) > 1, then anti-join it away. PostgreSQL rejects COUNT(DISTINCT variant) OVER (PARTITION BY unit_id) outright, since DISTINCT is not implemented for window functions; if you want the test inline, MIN(variant) OVER (PARTITION BY unit_id) <> MAX(variant) OVER (PARTITION BY unit_id) is the equivalent that does run.
- Do not resolve contamination by keeping the earliest variant. A unit that saw both arms carries treatment from both, so assigning it to either one biases that arm.
- Define the population as is_in_analysis_population = TRUE AND unit_type = 'user' AND first_exposed_at_utc < planned_end_utc AND first_exposed_at_utc + interval '7 days' <= :data_cut_utc. The horizon filter is what makes the readout reproducible next week instead of drifting with every re-run; the data-cut filter is the one that actually buys seven days of follow-up, since a unit exposed an hour before the horizon otherwise contributes an hour of observation to a seven-day rate.
- Measure the outcome on a per-unit relative window: LEFT JOIN fct_event on user_id with is_core_action = TRUE and occurred_at_utc in [first_exposed_at_utc, first_exposed_at_utc + interval '7 days'). LEFT JOIN so units with no outcome stay in the denominator at zero rather than being deleted by an inner join.
- Check the sample ratio before reading the effect: variant share of exposed units against the intended split, tested as a binomial. Run it on the truncated population as well as on the full exposed set, because if one arm exposes later on average the data-cut filter removes more of that arm and can manufacture a ratio mismatch the randomisation did not have. A mismatch on the full set means the exposure data is not a valid randomisation and invalidates the readout rather than being a footnote under it.
- Report the per-variant rate, the absolute difference, and the fact that the variance unit is unit_id. That is straightforward here only because the grain is already one row per user; a per-session outcome under user randomisation would need a delta-method or bootstrap standard error instead.
Worked solution 35 min
- Count units per variant and units with more than one variant; report the contaminated count before anything else.
- Build the exposed population CTE with both the horizon filter and the seven-days-before-data-cut filter, assert one row per unit_id, and record per variant how many units the second filter removed.
- LEFT JOIN the bounded outcome events and flag conversion with a boolean aggregate.
- Aggregate per variant to exposed, converted, rate and share, and run the binomial check on the share, both before and after the truncation.
- Re-run with a fixed calendar window from experiment_start_utc and record the difference as the size of the dilution you avoided.
Follow-up
- Some units were assigned days before they were exposed. What does analysing the assigned set instead do to the estimated effect, and in which direction?
- The split is 51/49 on 400,000 exposed units. Do you read the result?
- The treatment arm exposes on average two days later than control. What does that do to a fixed calendar outcome window, and which arm does it favour?
How would you prioritize features for a new product based on data-driv…
How would you prioritize features for a new product based on data-driven insights?
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- State what result would change your recommendation, so the answer is falsifiable.
- 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?
Present a hypothetical analysis of sales data to identify trends and a…
Present a hypothetical analysis of sales data to identify trends and actionable insights.
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Name one primary metric, then the guardrail that stops it being gamed.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
What steps would you take to evaluate the effectiveness of a new marke…
What steps would you take to evaluate the effectiveness of a new marketing campaign using data?
Approach
- Fix the population and the time window before naming any metric.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- 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?
Describe how you would analyze user engagement data to inform product …
Describe how you would analyze user engagement data to inform product improvements.
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- Fix the population and the time window before naming any metric.
- Decompose the metric into the rates that drive it, and say which one you would check first.
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 would you handle missing data in a dataset?
How would you handle missing data in a dataset?
Approach
- Say what you would check first and why it is the highest-information step.
- Work from the decision backwards to the evidence you would need.
- State your assumptions explicitly before working the problem.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Randomise a shared workspace feature without contaminating control
A feature changes a collaborative surface inside a workspace: when one member uses it, other members of the same account see the result in their own view. You have dim_user (user_id, account_id, is_internal), dim_account (account_id, seats_assigned, lifecycle_status) and fct_event. Among active accounts the mean seats_assigned is 6, the coefficient of variation of that count is 1.5, and the intraclass correlation of the weekly core-action rate within an account is 0.10. Choose the randomisation unit, quantify what that choice costs in sample, and specify how you would compute inference.
Approach
- State the interference before choosing anything: a treated user changes what an untreated colleague sees, so user-level randomisation puts both arms inside one account and biases the contrast toward zero. Randomise on account_id.
- Price the clustering properly. With equal clusters the design effect is 1 + (m - 1) rho = 1 + 5(0.10) = 1.5. Sizes here are far from equal, so use 1 + ((CV^2 + 1) m - 1) rho = 1 + (3.25 x 6 - 1)(0.10) = 2.85. The equal-size shortcut understates the cost by nearly half.
- Decide the estimand before the estimator. An account-weighted mean gives every workspace one vote; a user-weighted mean lets the largest workspaces dominate. With this size skew the two can move in opposite directions, so pick the one the decision needs and write it down.
- Compute standard errors on the account, not the user: cluster-robust on account_id, or collapse each account to a single number and test those. Below roughly 40 clusters per arm, cluster-robust errors are biased downward, so use a wild cluster bootstrap or randomisation inference over the assignment.
- Buy back variance where you can. Stratify assignment by seat band and lifecycle_status before randomising, and decide in advance how the handful of very large accounts are handled, since one enterprise workspace can carry more users than a hundred single-seat ones.
Worked solution 30 min
- Write the interference down: the outcome for user i depends on the treatment of other users in account(i), so the no-interference assumption fails at the user level and holds at the account level.
- Compute both design effects, 1.5 equal-size and 2.85 unequal-size, and use 2.85.
- Take the user-level sample requirement from the proportion shortcut, multiply by 2.85, then divide by the mean of 6 users per account to express it in accounts per arm.
- Specify the analysis: collapse to one row per account, regress the account-level outcome on variant with stratum fixed effects, and use a wild cluster bootstrap for inference.
- State the stopping rule up front: if the required account count exceeds the eligible population, the test is not runnable, and the alternatives are a longer window, a larger target effect, or a non-experimental read.
Follow-up
- Suppose the feature is not workspace-scoped but changes a globally shared ranking model, so no clean cluster exists. What design gets you a causal read, and what does it cost you?
- You have 900 eligible active accounts in total. Given the design effect, what absolute lift can this test detect, and is the honest answer 'do not run it'?
- The intraclass correlation is an estimate from last quarter. What happens to your sizing if the true value is 0.25?
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 ↗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 ↗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 ↗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 ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.
What is your approach to communicating complex data findings to non-te…
What is your approach to communicating complex data findings to non-technical stakeholders?
Approach
- Pick a story where you drove the decision, not one where you observed it.
- State the situation in two sentences and spend the rest on your reasoning.
- Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
Defend a flat experiment readout against a post-hoc segment
A feature you evaluated is flat on seven-day activation: +0.05pp with a 95% interval of [-0.47pp, +0.57pp], from 61,000 exposed users per arm in fct_experiment_exposure joined to dim_user and fct_event. Baseline activation is 32%. The launch team asks you to drop every surface except mobile_web, where the point estimate is +1.1pp, and re-run. You have ten minutes in their planning meeting. Deliver a spoken position: what you will and will not do, and the decision you recommend.
Approach
- Recognise what is being probed: whether you hold a statistical position under social pressure without becoming either rigid or apologetic. A generic answer says the segment is not significant; a strong one separates the request into a question that is answerable (is the mobile_web number real?) and one that is not (can we ship on it?), and answers both.
- Price the multiplicity out loud. The slice was chosen after seeing the results, so its estimate is selected on favourable noise and is biased away from zero. With k independent looks at a nominal 5% level, the chance of at least one false positive is 1 - 0.95^k: 26% at six segments, 64% at twenty. Quote the k you actually inspected, not the k you reported.
- Use the arithmetic already in front of you. On the point estimates, a +1.1pp mobile_web effect combined with a pooled +0.05pp implies the remaining surfaces average negative in proportion to mobile_web's share of exposures. State that as a testable implication of their story rather than as a rebuttal of it.
- Ask the one question that settles the category: was mobile_web named in the analysis plan before launch? If it was, it is a planned comparison and gets a corrected reading. If it was not, it is a hypothesis, and the honest move is to size the test that would confirm it.
- Convert the refusal into a cost. Size a mobile_web-only confirmatory test at the claimed effect, state the weeks of mobile_web traffic it needs, and close with the recommendation: do not ship this as a lift, and note that the interval already rules out anything at or above +0.6pp, which is itself a useful input to the roadmap.
Follow-up
- The confirmatory test you sized needs nine weeks of mobile_web traffic and the team has three. What do you recommend instead?
- Suppose mobile_web was pre-registered. How does your reading change, and what correction do you apply?
- Your interval excludes +0.6pp. Is that the same as saying the feature does nothing?
Choose between three teams' requests with one analyst-week
You have one analyst-week. Three requests land the same morning. A growth team wants a paid-channel readout before a Friday spend decision. A billing team wants gross monthly revenue churn rebuilt, because the current figure recognises cancellation at canceled_at_utc rather than period_end_utc and is therefore wrong. A product team wants a dashboard for a feature launching in six weeks. All three sponsors are peers of your manager. Deliver your ranking, the explicit rule that produced it, and the message you send to the two teams you defer.
Approach
- Recognise what is being probed: whether you prioritise on decision value and reversibility or on who asked most recently and most loudly. The generic answer sorts by importance; the strong one states a rule, applies it, and accepts the ranking it produces even where that is uncomfortable.
- Score each request on three statable things: the decision it unblocks and the date that decision is made, the cost of being wrong in the meantime, and whether the work is one-off or compounding. A wrong published churn figure compounds, because it is quoted downstream and enters forecasts; the channel readout has a fixed date that cannot move; the dashboard has six weeks of slack.
- Notice the tension between value and urgency rather than resolving it by feel. The churn defect is the most valuable item and the least urgent one, which is exactly the shape of work that never gets done.
- Break the churn item in two. A one-hour severity check, sizing the gap between the two recognition points in MRR, is cheap enough to do before ranking anything and may promote the item outright. Do that first, then rank.
- Make the deferrals concrete. Each deferred team gets a date, a reason expressed as another team's decision deadline rather than as relative importance, and the smallest thing you can hand them immediately.
Follow-up
- The dashboard team escalates to your manager. What do you say in that conversation?
- Your severity check shows churn is overstated by 15%. Does the ranking change, and does anybody need to be told today regardless of the ranking?
- A fourth request arrives Wednesday with a Thursday deadline. What comes off the list, and who do you tell first?
- 01
What is your approach to communicating complex data findings to non-technical stakeholders?
- 02
A feature you evaluated is flat on seven-day activation: +0.05pp with a 95% interval of [-0.47pp, +0.57pp], from 61,000 exposed users per arm in fct_experiment_exposure joined to dim_user and fct_event. Baseline activation is 32%. The launch team asks you to drop every surface except mobile_web, where the point estimate is +1.1pp, and re-run. You have ten minutes in their planning meeting. Deliver a spoken position: what you will and will not do, and the decision you recommend.
- 03
You have one analyst-week. Three requests land the same morning. A growth team wants a paid-channel readout before a Friday spend decision. A billing team wants gross monthly revenue churn rebuilt, because the current figure recognises cancellation at canceled_at_utc rather than period_end_utc and is therefore wrong. A product team wants a dashboard for a feature launching in six weeks. All three sponsors are peers of your manager. Deliver your ranking, the explicit rule that produced it, and the message you send to the two teams you defer.
Is this an official 6sense interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at 6sense. Rounds and questions reflect what candidates have reported, not a process 6sense has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the interview process?
The interview process for a Data Scientist at 6sense is rigorous, typically requiring a solid understanding of technical concepts and problem-solving skills. Candidates can expect to engage in coding challenges, case studies, and behavioral interviews.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates often demonstrate a strong analytical mindset, effective communication skills, and a collaborative approach to problem-solving. They can articulate their thought processes and findings clearly to both technical and non-technical stakeholders.
PracHub interview research ↗What is the culture and working style at 6sense?
6sense fosters a collaborative and innovative culture where data-driven decision-making is valued. Employees are encouraged to share ideas, learn from one another, and contribute to a supportive team environment.
PracHub interview research ↗What is the typical timeline from initial screen to offer?
The entire process can take several weeks, depending on scheduling and the number of candidates. Generally, you can expect to receive feedback within a week after each stage.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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