A Data Scientist at Lumen plays a critical role in harnessing the power of data to drive impactful business decisions and enhance customer experiences. This position involves analyzing vast datasets to extract meaningful insights, develop predictive models, and inform strategic initiatives across various teams and products. You will contribute to projects that span network optimization, customer analytics, and operational efficiency, making data-driven decisions that directly affect the company's growth and innovation.
As a Data Scientist, you will engage with cross-functional teams, leveraging your analytical expertise to solve complex challenges. The role requires an understanding of both technical methodologies and business acumen, as you will be tasked with translating complex data findings into actionable strategies. Your work will not only influence product developments but also enhance user experiences by optimizing services based on data insights. This dynamic and influential position is crucial for Lumen, where technology and data converge to deliver superior communication services.
HireVue Screening
reportedRounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.
What to demonstrate
- Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
- The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
- Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options
How to prepare
- Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
- Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
- Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.
Technical Assessments
reportedBefore anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.
What to demonstrate
- Whether an ambiguous term becomes a specific column and filter before any computation happens
- Whether you read the schema for keys and cardinality rather than only for column names
- Whether the result answers the question at the grain it was asked at, per user or per session or per day
How to prepare
- Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
- On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
- Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over
1 candidate reports. Individual accounts describe a particular role and hiring cycle.
Lumen Intern Data Analyst Interview Experience — Online Assessment Is All Behavioral Scenarios and a Recorded Presentation
The questions weren't hard. I applied for the analyst position — even though it lists requirements like SQL, it doesn't really seem like a technical role, so it's basically all testing behavior. As soon as you apply you get an OA. It also seems like this one can be a remote internship, which is a nice touch. The general question types were: given an article, then multiple choice questions on it;…
Read full experiencePracHub 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.
Comparing cohort retention curves of different maturities, or building the curve from users who are still present
A cohort four weeks old has no week-8 value, so an average taken across cohorts silently drops young cohorts from the later columns and keeps them in the earlier ones. The curve then bends upward at the tail, and the reading that 'retention is improving over time' is an artefact of which cohorts survived to be measured. The same error appears in the denominator when retention is computed over users active in the current period rather than over the full original cohort, which conditions on survival and guarantees a flattering number. The fix is a triangle: fix the cohort at signup, bound every window on both sides, and only compare cells where every cohort has had the full elapsed time, publishing the rest as blank rather than as a partial average.
Reading experiment results before checking the arm split
Compare observed arm counts against the intended allocation ratio, not an assumed even split, and set the alarm far below the conventional 0.05: at 0.05 roughly one healthy experiment in twenty trips it, which is why sample-ratio checks usually run at p < 0.001 or stricter. The test's power scales with sample size, so it misses a real diversion on a small experiment and fires on an imbalance too small to move the estimate on a very large one. A flag means go find the assignment or logging fault before reading any outcome, not report a mismatch.
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.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain your thought process in developing a predictive model for cust…
Explain your thought process in developing a predictive model for customer churn.
Approach
- Set a baseline first, so any model has something honest to beat.
- Say how the offline result would be validated online before it is trusted.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
Follow-up
- What would you monitor after launch to know the model is still valid?
- How would you choose the decision threshold, and who owns that choice?
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.
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?
Sessionise an event stream with gap and midnight rules
Sessionise a raw event stream. Input: a DataFrame with visitor_id, user_id (often NULL), occurred_at_utc and event_name, unsorted, up to 5 million rows. A session breaks when the gap from that visitor's previous event exceeds 30 minutes, and is force-closed at UTC midnight so no session spans two calendar dates. A gap of exactly 30 minutes does not break. Emit one row per session with session_id, visitor_id, the user_id as of the last event in the session, started_at_utc, ended_at_utc, session_date, duration_seconds and event_count. Vectorise; do not loop per visitor.
Approach
- Sort by ['visitor_id', 'occurred_at_utc', 'event_id'] once, then express the whole problem as one boolean vector: a row starts a new session when the visitor changed, or the gap exceeds 30 minutes, or the UTC date differs from the previous row's UTC date. Cumsum that vector and you have the session key.
- Get the comparison direction right on the gap: the rule is strictly greater than 1800 seconds, so an event at exactly 1800 seconds continues the session. Write it as gap > pd.Timedelta(minutes=30), and make the tie a test case rather than an assumption.
- Derive the midnight break from the date change, not from inserting synthetic boundary rows. A date change implies a break even when the gap is two seconds, which is precisely the force-close rule and is why the two conditions are ORed rather than one subsuming the other.
- Aggregate with a single groupby on the session key: min and max of occurred_at_utc, size for event_count, and last for user_id, which is correct because the frame is already sorted so 'last' is the final event in the session. That is the identity-as-of-session-end rule.
- Compute duration_seconds as (max - min).dt.total_seconds(), which makes a single-event session 0 seconds. Say so explicitly, because a downstream mean session duration is sensitive to whether single-event sessions are 0 or excluded.
Worked solution 35 min
- df = df.sort_values(['visitor_id','occurred_at_utc','event_id']).reset_index(drop=True).
- new_visitor = df.visitor_id.ne(df.visitor_id.shift()); gap = df.occurred_at_utc.diff(); new_day = df.occurred_at_utc.dt.date.ne(df.occurred_at_utc.dt.date.shift()).
- is_start = new_visitor | (gap > Timedelta(minutes=30)) | new_day; df['session_key'] = is_start.cumsum().
- g = df.groupby('session_key'); out = g.agg(visitor_id=('visitor_id','first'), user_id=('user_id','last'), started_at_utc=('occurred_at_utc','min'), ended_at_utc=('occurred_at_utc','max'), event_count=('event_id','size')).
- out['session_date'] = out.started_at_utc.dt.date; out['duration_seconds'] = (out.ended_at_utc - out.started_at_utc).dt.total_seconds(); assign session_id from the sorted index.
Follow-up
- Sessions are used as the denominator of a conversion rate. How does moving the inactivity gap from 30 to 45 minutes move that rate, and in which direction?
- A visitor's clock is 40 minutes ahead, so their events arrive with future occurred_at values. What does your sessioniser do, and what would you rather it did?
- The same person signs up mid-session on mobile and continues on desktop. How many sessions and how many users does your output show, and is that the right answer?
Can you demonstrate how to handle missing values in a dataset?
Can you demonstrate how to handle missing values in a dataset?
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- 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.
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 the difference between supervised and unsupervised learning wi…
Explain the difference between supervised and unsupervised learning with examples.
Approach
- State the window function and its partition and ordering out loud before writing it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Weekly visit-to-signup conversion split by acquisition channel
From fct_session (session_id, visitor_id, started_at_utc, referrer_channel, is_bot_flagged, consent_state) and fct_event (visitor_id, occurred_at_utc, event_name), compute visit-to-signup conversion for one ISO week, split by channel. session_id is the unique key of fct_session. Denominator: distinct visitor_id with a session starting in the week, is_bot_flagged = FALSE and consent_state <> 'denied'. Numerator: those visitors with a 'signup_completed' event in the same week. Label each visitor with the referrer_channel of their first session in the window. Return channel, visitors, signups and rate, plus one all-channel total row.
Approach
- Build a visitor spine that is one row per visitor: filter sessions to the week, drop is_bot_flagged and consent_state = 'denied', then take the first session per visitor with ROW_NUMBER() OVER (PARTITION BY visitor_id ORDER BY started_at_utc, session_id) = 1 to carry the channel label. Collapsing to one row here is what makes the channel buckets mutually exclusive and the totals additive.
- session_id is the unique key, so that ordering is total and the label is reproducible. If the table carried no unique key you would have to write an explicit tie rule instead, because two sessions on different channels at the identical timestamp would otherwise label the visitor differently between runs.
- Attach the outcome as a semi-join (EXISTS on a signup_completed event for that visitor inside the same week) rather than a join to the event table, so a visitor who fires the event twice does not count twice and inflate the numerator past the denominator.
- Aggregate with COUNT() as visitors and COUNT() FILTER (WHERE signed_up) as signups, and compute the rate as signups::numeric / NULLIF(visitors, 0) so an empty channel returns NULL rather than a division error.
- Produce the total with GROUP BY GROUPING SETS ((channel), ()), which re-sums numerator and denominator for the total row. Averaging the channel rates gives a different and wrong number whenever channel volumes differ, which they always do.
- Verify the spine before trusting the output: COUNT(*) must equal COUNT(DISTINCT visitor_id), and the per-channel visitor counts must sum to the total row.
Worked solution 20 min
- Write the filtered session CTE and check its row count against an unfiltered count, so you know how much volume the bot and consent filters removed.
- Add the ROW_NUMBER first-session pick and assert one row per visitor.
- Add the EXISTS outcome flag and aggregate with FILTER.
- Add GROUPING SETS for the total and format the rate to four decimal places.
- Spot-check one channel by hand: pull its visitor list, count signups directly, compare.
Follow-up
- The denominator is distinct visitors. If a browser release shortens cookie lifetime, what happens to this rate, and how would you tell that apart from a genuine drop?
- A visitor's first session is direct and their signup session is paid search. Your label says direct. When is that the wrong answer for the decision being made?
- How do you roll four weeks into a month, and why is averaging the four weekly rates wrong?
Present a case where you used data to solve a business problem and the…
Present a case where you used data to solve a business problem and the results achieved.
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Restate the decision this analysis has to support, and who acts on the answer.
- Fix the population and the time window before naming any metric.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
How do you prioritize your tasks when working on multiple projects?
How do you prioritize your tasks when working on multiple projects?
Approach
- 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.
- Fix the population and the time window before naming any metric.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
If provided with customer data, how would you approach predicting futu…
If provided with customer data, how would you approach predicting future behavior?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- 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?
- How would you detect that the metric is being gamed rather than genuinely improving?
Given a dataset, how would you identify key trends and insights?
Given a dataset, how would you identify key trends and insights?
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- Fix the population and the time window before naming any metric.
- State what result would change your recommendation, so the answer is falsifiable.
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?
How would you design an experiment to test the effectiveness of a new …
How would you design an experiment to test the effectiveness of a new feature?
Approach
- Say whether units interfere with each other, and switch design if they do.
- Decide the analysis before seeing data, including how long it runs and when you look.
- State the primary metric and the minimum effect worth shipping, then size the test.
Follow-up
- How would you handle interference between treated and control units?
- What would you conclude if the result is positive but the test is underpowered?
Can you explain a complex data-related project you've worked on and th…
Can you explain a complex data-related project you've worked on and the impact it had?
Approach
- Work from the decision backwards to the evidence you would need.
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Estimate a threshold-triggered programme with regression discontinuity
Accounts reaching seats_licensed >= 25 in dim_account are automatically assigned a dedicated onboarding specialist; below 25 they are not. Leadership wants the programme's effect on 12-month net revenue retention and will not randomise coverage away from any account. Three years of dim_account and fct_subscription_period rows are available, including mrr_cents_constant_fx. Specify the design, the estimand it identifies, two threats that would invalidate it, and what changes when 8% of accounts below the threshold received a specialist anyway.
Approach
- Set up a regression discontinuity on the running variable seats_licensed with a cutoff at 25, comparing accounts just below with accounts just above. The identifying assumption is continuity: absent the programme, expected 12-month NRR would be a continuous function of seat count through 25.
- Name the estimand honestly and early. This is a local average treatment effect at 25 seats. It says nothing about a 5-seat or a 200-seat account, and that belongs in the first line of the answer rather than a footnote.
- Estimate with local linear regression on each side, a triangular kernel, an MSE-optimal bandwidth and robust bias-corrected confidence intervals. Do not fit a high-order global polynomial; it imports weight from observations far from the cutoff and is known to manufacture discontinuities.
- Test the two threats that actually apply. Manipulation: an account that wants the specialist can buy a 25th seat, which piles density just above the cutoff, so run a density test on seats_licensed around 25 and look for a spike at exactly 25. Bundling: if a price break, a plan tier or a support SLA also switches at 25 seats, the discontinuity measures the whole bundle, so check current_plan_tier and the price schedule at the cutoff.
- Treat the 8% crossover as a fuzzy design. Treatment probability jumps at the cutoff without going from 0 to 1, so divide the jump in NRR by the jump in the probability of receiving a specialist. That is a Wald instrumental-variables estimator with the cutoff indicator as the instrument; it needs exclusion, which is exactly what the bundling check is about, plus monotonicity, and it narrows the estimand further to compliers at the cutoff.
Worked solution 45 min
- Fix the running variable as seats_licensed at the moment the assignment rule was evaluated, and fix the outcome as 12-month NRR computed from fct_subscription_period on mrr_cents_constant_fx for the account's cohort.
- Plot mean NRR in one-seat bins on each side of 25 with a local linear fit. The picture comes before the estimate, because a discontinuity invisible in the binned plot is rarely real.
- Run the density test at 25 and a continuity check on pre-cutoff characteristics such as billing_country, account_type and pre-programme MRR; these must be smooth through the cutoff.
- Estimate the sharp RD with an MSE-optimal bandwidth and robust bias-corrected intervals, then repeat at half and double the bandwidth as a sensitivity check.
- Estimate the first stage, the jump in specialist assignment at 25, and report the fuzzy estimate as the ratio with the estimand stated as the complier effect at the cutoff.
Follow-up
- The density test shows a spike at exactly 25 seats. Is the design dead, and what would you do next?
- You have 40,000 accounts but the bandwidth keeps 900. How does the detectable effect compare with a randomised comparison of the same nominal size?
- Seat counts change over time. Which value of seats_licensed is the running variable, and what breaks if you pick the wrong one?
Tell a pipeline outage from a collapse in usage
Weekly active accounts completing a core action fell 9%, and almost the entire fall sits in accounts whose events carry surface = 'ios'. App crash rates and store reviews are unchanged. You have fct_event with occurred_at_utc, received_at_utc, event_name, is_core_action, app_version and surface, plus fct_session and the ingestion job run log. Establish within the hour whether iOS engagement fell or iOS events stopped arriving, name the evidence that distinguishes them, and say what you would publish on the dashboard in the meantime.
Approach
- Compare the event-name composition inside surface = 'ios' against the prior four weeks as shares, not counts. A behaviour collapse scales most event names together; a dropped event definition or a broken downstream filter hits specific event_name values while page_view and session-opening events hold steady. That shape difference is the fastest discriminator available.
- Profile the received_at_utc minus occurred_at_utc distribution per day for surface = 'ios'. A stalled-then-backfilling pipeline shows a fat upper tail and a recovering p99; a silently dropped stream shows an unchanged lag distribution over a smaller volume. The two failure modes have different remedies and different histories.
- Cut by app_version. A logging SDK change arrives with one build and ramps with its adoption curve; an infrastructure fault arrives across every build within the same hour. Checking this costs one group-by and rules out half the hypothesis space.
- Cross-check against a signal that does not travel the suspect path: server-emitted events with session_id NULL, and subscription or billing activity for the same accounts. If those accounts are still transacting, the users did not leave.
- Publish an ex-iOS total with an explicit annotated break rather than a blended total. A blended number during a known ingestion gap is wrong in a direction you can already name, and republishing it daily spreads the artefact into every downstream report.
Follow-up
- Suppose the events do eventually backfill. What is your policy for restating the published weekly numbers, and who needs to be told?
- What monitor would have caught this before a human noticed the weekly metric, and what would it alert on?
- If is_core_action is maintained in the tracking plan, what governance would stop a change to that list from silently moving a north-star metric?
For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Design one test end to end on paper
- Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
- Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
- State in advance what you will do if the primary metric is flat while a secondary metric is significant.
Deliverable: A one-page test design with a decision rule written before launch.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Power arithmetic until it is automatic
- Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
- Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
- Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.
Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Variance and the unit-of-analysis problem
- Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
- Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
- Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.
Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04Validity threats you can actually test for
- Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
- Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
- Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.
Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.
Practice prompt ↗Practice prompt ↗Worked solution ↗05When randomization is not available
- Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
- Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
- List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.
Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.
Practice prompt ↗Practice prompt ↗06The readout query
- Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
- Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
- Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.
Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.
Practice prompt ↗Practice prompt ↗07Present it to someone who will not read the appendix
- Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
- Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
- Rewrite your opening line so the recommendation lands before any methodology.
Deliverable: A one-page readout whose first line is the recommendation.
Practice prompt ↗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.
Describe a time you faced a significant challenge in a project. How di…
Describe a time you faced a significant challenge in a project. How did you overcome it?
Approach
- Quantify the outcome, including what you would not claim credit for.
- State the situation in two sentences and spend the rest on your reasoning.
- Close with what you would do differently, concretely.
Follow-up
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
Disagree with a product manager'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?
Explain a wide interval to a non-technical executive
A pricing change is under consideration. Your best estimate of its effect on trial-to-paid conversion is a 1.8pp drop, with a 95% interval from a 4.6pp drop to a 1.0pp rise, read from a geo holdout rather than a randomised test. An executive preparing a board slide asks you for 'the number'. You have ninety seconds and one slide, and the words confidence interval, p-value and significance are not usable with this audience. Deliver the slide headline, the single supporting line, and what you say aloud.
Approach
- Recognise what is being probed: whether you can carry uncertainty into a decision instead of either hiding it or hiding behind it. The generic answer promises to explain the interval in plain English; the strong one replaces the question 'what is the number' with 'across this range, where does the decision change'.
- Find the threshold before you draft anything. Ask what the pricing case assumes, then compute the conversion drop at which the higher price stops adding revenue: price uplift on the conversions kept against the revenue lost from conversions forgone. That single figure is what makes the range legible.
- Restate the estimate and both bounds in the unit the audience already reasons in. Convert percentage points into monthly first-paid conversions at current trial volume, then into mrr_cents_constant_fx, so the slide reads as money per month rather than as statistics.
- Place the range against the break-even and say which part of it sits on each side. If most of the range clears the threshold, that is a recommendation to proceed with a monitoring plan; if the range straddles it, that is a recommendation to narrow the range first.
- Name what would narrow it and what that costs in weeks, then give one recommendation with an explicit condition for revisiting it. Uncertainty stated without a next step is read as indecision and the midpoint gets used anyway.
Follow-up
- The executive says to give the midpoint and they will manage the risk. What do you do?
- How does the slide change if the interval were a 4.6pp to 0.2pp drop, with no positive outcomes in range?
- Why is a geo holdout the credible read here rather than the attributed channel numbers you already have?
- 01
Describe a time you faced a significant challenge in a project. How did you overcome it?
- 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
A pricing change is under consideration. Your best estimate of its effect on trial-to-paid conversion is a 1.8pp drop, with a 95% interval from a 4.6pp drop to a 1.0pp rise, read from a geo holdout rather than a randomised test. An executive preparing a board slide asks you for 'the number'. You have ninety seconds and one slide, and the words confidence interval, p-value and significance are not usable with this audience. Deliver the slide headline, the single supporting line, and what you say aloud.
Is this an official Lumen interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Lumen. Rounds and questions reflect what candidates have reported, not a process Lumen has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗What is the typical interview difficulty for the Data Scientist position at Lumen?
The interviews are generally considered to be of average difficulty, though some candidates report challenging questions, particularly in technical areas. Expect a mix of behavioral and technical questions.
PracHub interview research ↗How can I differentiate myself from other candidates?
Successful candidates often demonstrate strong problem-solving skills, effective communication, and a collaborative mindset. It's crucial to articulate your experiences clearly and how they relate to the role.
PracHub interview research ↗What is the company culture like at Lumen?
Lumen fosters a culture of innovation and teamwork, emphasizing data-driven decision-making and collaboration across teams. Your ability to align with these values will be assessed throughout the interview process.
PracHub interview research ↗What is the typical timeline from application to offer?
The timeline can vary, but candidates usually hear back within a few weeks after the initial screening. If you progress to further rounds, you may receive feedback more quickly.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
Explore related engineering environments and compare the preparation focus.
Zomato Data Scientist Interview Questions & Guide 2026
SQL sets for data scientist prep ahead of Zomato, covering window functions, funnel and retention queries, and the joins that quietly drop the rows you needed.
Zocdoc Data Scientist Interview Questions & Guide 2026
Communication practice for Zocdoc data scientist candidates: turn an analysis into a recommendation, name the uncertainty honestly, and answer the follow-up a skeptical stakeholder will raise.
Zendesk Data Scientist Interview Questions & Guide 2026
SQL sets for data scientist prep ahead of Zendesk, covering window functions, funnel and retention queries, and the joins that quietly drop the rows you needed.
Yum! Brands Data Scientist Interview Questions & Guide 2026
Data-quality drills for Yum! Brands data scientist prep: survivorship and selection bias, leakage through time-ordered features, and extreme values where provenance decides the call. A logging fault or unit slip gets corrected or dropped; a