Lattice · Data Scientist
Updated · 2026-09-22

Lattice Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

The role of a Data Scientist at Lattice is fundamentally centered around leveraging data to drive insights that inform decision-making and enhance user experiences. As a Data Scientist, you will work at the intersection of data analysis, machine learning, and product strategy, allowing you to make a significant impact on the organization’s products and services. This position is critical in helping the company understand user behavior, optimize processes, and ultimately deliver better solutions for its clients.

When randomisation is off the table, the skill being checked is naming an identification strategy together with the assumption it rests on: parallel trends for difference-in-differences, relevance and exclusion for an instrument, overlap and conditional ignorability for matching. Say the assumption out loud and say how you would try to break it.

Lattice candidates report 3 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Decompose a metric move by segment and mixDefine numerator, denominator and window preciselyPick a randomisation unit that respects interference

35 min read

Browse Data Scientist questions

See the practice prompts

16Practice promptsAcross five skill areas
3With worked solutionsIncluded in the practice prompts

The role of a Data Scientist at Lattice is fundamentally centered around leveraging data to drive insights that inform decision-making and enhance user experiences. As a Data Scientist, you will work at the intersection of data analysis, machine learning, and product strategy, allowing you to make a significant impact on the organization’s products and services. This position is critical in helping the company understand user behavior, optimize processes, and ultimately deliver better solutions for its clients.

In this role, you will collaborate closely with product managers, engineers, and other stakeholders to analyze complex data sets, develop predictive models, and communicate findings that shape product development and business strategies. The complexity of data you will encounter, coupled with the strategic influence you wield through your insights, makes this role not only vital but also intellectually rewarding. You will contribute to products that enhance performance management and employee engagement, ensuring that Lattice remains a leader in the HR tech space.

01

Initial Screening Calls

reported

A screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.

What to demonstrate

  • Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
  • Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
  • Whether timeline, location and compensation expectations make the rest of the loop worth scheduling

How to prepare

  • Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
  • Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
  • Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice
PracHub interview research ↗
02

Technical Skills Interview

reported

Much of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.

What to demonstrate

  • Whether the query you write matches the plan you just described
  • What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
  • Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly

How to prepare

  • Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
  • Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
  • Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock
PracHub interview research ↗
03

Cultural Fit Interview

reported

Because the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.

What to demonstrate

  • Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
  • Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
  • Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs

How to prepare

  • For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
  • Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
  • For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.
PracHub interview research ↗

PracHub editorial advice for the preparation topics above.

01

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.

02

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.

03

Defining the cohort on a post-treatment condition

Ask how rows entered the table. Filtering on something that treatment itself influences, such as users who finished onboarding or accounts still active at ninety days, breaks comparability between arms; define the population at an entry point that precedes exposure and keep everyone in it.

04

Interpreting a change before checking data quality and logging

Spend the first pass on row volume by day, null rates, duplicate keys, and whether the step change lands on a release or tracking-migration date. A discontinuity that coincides with a deploy is an instrumentation hypothesis before it is a behavioural one.

Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.

13 technical prompts3 include a worked solution

If your model underperforms, what steps would you take to diagnose the…

medium
machine learning and modelling

If your model underperforms, what steps would you take to diagnose the issue?

Approach
  1. Say how the offline result would be validated online before it is trusted.
  2. Check what information would not exist at prediction time, and exclude it.
  3. Frame the prediction: the label, the moment of prediction, and the action it triggers.
Follow-up
  • Where could label leakage enter this setup?
  • What would you monitor after launch to know the model is still valid?

Can you explain regularization and why it is important in machine lear…

medium
machine learning and modelling

Can you explain regularization and why it is important in machine learning?

Approach
  1. Frame the prediction: the label, the moment of prediction, and the action it triggers.
  2. Say how the offline result would be validated online before it is trusted.
  3. Pick an evaluation metric that matches the cost of each error type, not a default.
Follow-up
  • What would you monitor after launch to know the model is still valid?
  • How would you choose the decision threshold, and who owns that choice?

Split a pooled conversion drop into rate and mix

mediumWorked solution
mix shiftdecompositionpandas

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
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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
  1. 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.
  2. 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.
  3. rate_effect = (w0 * (r1 - r0)).sum(); mix_effect = ((w1 - w0) * r0).sum(); interaction = ((w1 - w0) * (r1 - r0)).sum().
  4. 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.
  5. assert abs(rate + mix + interaction - (pooled1 - pooled0)) < 1e-12.
EXPECTED RESULTThree scalars summing exactly to the observed pooled change, plus a segment table whose contribution column also sums to that change. In the stated case rate and mix carry opposite signs: several segments converted better while volume moved toward a weaker-converting one, which is what makes the pooled move look inexplicable until it is split.
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?

Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.

Small steps. Visible outcomes.0 / 7 completed
ONE WEEK · YOUR PACE

Prepare, practise & reflect

One practical outcome each day. Spend longer where you need it.

0 / 7 done
01Breadth pass: query fluency
  • Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
  • For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
  • Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.

Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.

Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗
02Breadth pass: statistics and inference
  • Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
  • Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
  • Rewrite the two weakest answers the following morning from memory in full sentences.

Deliverable: Ten graded answers with an honest count of exact hits.

Practice prompt ↗Practice prompt ↗Practice prompt ↗
03Breadth pass: modelling
  • Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
  • Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
  • Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.

Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.

Practice prompt ↗Practice prompt ↗
04Breadth pass: product judgement
  • Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
  • For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
  • Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.

Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.

Practice prompt ↗Practice prompt ↗Worked solution ↗
05Depth, first area
  • Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
  • Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
  • Re-solve the two you failed the same evening with notes closed.

Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.

Practice prompt ↗Practice prompt ↗
06Depth, second area, and the seam between them
  • Repeat the depth protocol on the second-ranked area with the same six-problem structure.
  • Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
  • Solve your own combined problem end to end and note where the handoff between the two areas cost you time.

Deliverable: One combined problem, solved end to end, with the handoff failure written down.

Practice prompt ↗Practice prompt ↗
07Integration and re-measurement
  • Re-run the six prompts from day one under the same clock and compare both correctness and time.
  • Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
  • Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.

Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.

Practice prompt ↗Practice prompt ↗Worked solution ↗

Expand any day for tasks and deliverables. Your progress is saved on this device.

A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.

Describe a time when you had to communicate complex data findings to a…

medium
behavioural and stakeholder questions

Describe a time when you had to communicate complex data findings to a non-technical audience.

Approach
  1. Name the disagreement or constraint, and how you resolved it with evidence.
  2. Quantify the outcome, including what you would not claim credit for.
  3. State the situation in two sentences and spend the rest on your reasoning.
Follow-up
  • How did you know the outcome was caused by your change?
  • What did you decide not to do, and why?

Disagree with a product manager's roadmap claim using data

medium
causal inferenceselection biasstakeholder

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
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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?

Choose between three teams' requests with one analyst-week

medium
prioritisationstakeholderjudgement

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
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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

    Describe a time when you had to communicate complex data findings to a non-technical audience.

  • 02

    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.

  • 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.

PracHub interview preparation framework ↗
Is this an official Lattice interview guide?

No. It is PracHub's own research and practice material for the Data Scientist role at Lattice. Rounds and questions reflect what candidates have reported, not a process Lattice has published, and they change over time. Confirm the current format and scope with your recruiter.

PracHub interview research ↗
How difficult are the interviews for this position?

The interviews are generally considered to be of average difficulty, focusing on both technical and behavioral aspects. Preparation is key, and candidates should be ready to demonstrate their problem-solving skills and cultural fit.

PracHub interview research ↗
What differentiates successful candidates?

Successful candidates typically showcase a strong mix of technical proficiency, effective communication skills, and a collaborative spirit. They are also able to connect their work to the larger goals of the organization.

PracHub interview research ↗
What is the culture and working style at Lattice?

Lattice fosters a collaborative and inclusive culture, emphasizing user-centric approaches. Team members are encouraged to share ideas and contribute to projects in a way that reflects the company's values.

PracHub interview research ↗
What is the typical timeline from initial screen to offer?

The timeline can vary, but candidates can typically expect a decision within 4-6 weeks from the initial application, depending on the team’s hiring needs.

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.