As a Data Scientist at Taglynk, you are at the intersection of high-impact consumer product innovation and enterprise-grade analytics. Whether you are working on B2C growth initiatives or B2B SaaS enterprise solutions, your role is to transform raw, large-scale behavioral and transactional data into actionable intelligence. You aren't just building models; you are solving fundamental business problems that drive personalization, revenue, and product strategy.
You will function as a strategic partner to Product, Engineering, and Growth teams. The work is deeply technical—involving Deep Learning, CNNs/Transformers, and predictive forecasting—but it is also inherently narrative. Taglynk values individuals who can bridge the gap between complex statistical rigor and clear business storytelling, ensuring that the insights you uncover are not just accurate, but also usable by stakeholders to make high-stakes decisions.
The role requires an "ownership mindset." You are expected to own the entire model lifecycle, from the initial problem framing and data preparation to deployment and performance monitoring in production environments.
Application Review
reportedA 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
Technical Screening
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
Problem-Solving Sessions
reportedBecause 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.
Cultural Alignment Discussion
reportedAn extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.
What to demonstrate
- Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
- Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
- How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one
How to prepare
- Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
- Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
- Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.
Final Decision
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.
Computing monthly churn against the entire customer base when contracts are annual
An annual contract has no opportunity to churn except at its renewal date, so an account that is eleven months from renewal is in the denominator while being incapable of appearing in the numerator. The resulting rate is smaller than the real one by roughly the ratio of the base to the renewal-eligible base, and it oscillates with the seasonality of when deals were originally signed rather than with anything about the customers. The corresponding trap on the other side is counting a churn on the date the record was updated rather than on term_end_date, which shifts losses into whichever month the operations team did its paperwork.
Reporting a mean over accounts when account revenue is heavy-tailed
When a small number of accounts hold most of the revenue, the sample mean is dominated by whichever of them happens to be in the sample, and the sample variance keeps growing as more data arrives instead of stabilising. In that regime the usual central-limit-based confidence interval understates uncertainty, and a single renewal or a single large account's batch job can flip the sign of a measured effect. The fixes are to pre-register a winsorisation or capping rule before looking at the outcome, to report account counts crossing a threshold alongside the revenue figure, or to define the estimand on a bounded transform. Choosing the cap after seeing the result is a separate and worse problem, because the cap then encodes the answer.
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.
Treating a non-significant result as proof of no effect
Say whether the confidence interval excludes the effect sizes you would have cared about. If it does not, the honest reading is that the test was underpowered, so report the minimum detectable effect the design could have found and what sample size would resolve it.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the trade-offs between model interpretability and predictive a…
Explain the trade-offs between model interpretability and predictive accuracy in a production environment.
Approach
- Check what information would not exist at prediction time, and exclude it.
- Pick an evaluation metric that matches the cost of each error type, not a default.
- 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?
Describe your approach to feature engineering when working with sparse…
Describe your approach to feature engineering when working with sparse, high-dimensional behavioral data.
Approach
- Say how the offline result would be validated online before it is trusted.
- Check what information would not exist at prediction time, and exclude it.
- Pick an evaluation metric that matches the cost of each error type, not a default.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- What would you monitor after launch to know the model is still valid?
Sessionise an API event stream with a 30-minute inactivity gap
fct_api_request arrives as a DataFrame with account_id, user_id, request_at (tz-aware UTC), traffic_class and http_status, roughly 5 million rows. Assign a session_id to every human-attributable request: drop rows where user_id is null or traffic_class is in ('ci','synthetic_monitor','load_test'), then open a new session whenever the gap since that user's previous remaining request exceeds 30 minutes. Return the filtered frame plus session_id, and a per-session summary with user_id, account_id, session start, session end and request count. Do not loop over rows.
Approach
- Settle the filter-then-gap ordering before writing code. Removing CI and synthetic rows changes the gaps, so sessionising the raw stream and filtering afterwards is a different answer; the definition given filters first, and the two diverge most for accounts whose CI runs every ten minutes.
- Sort once by (user_id, request_at) with a stable kind, then gap = df.groupby('user_id', sort=False).request_at.diff(). The first row of each user yields NaT, which is exactly the boundary condition you want rather than a special case to patch.
- new_session = gap.isna() | (gap > Timedelta(minutes=30)); session_id = new_session.cumsum(). The cumsum runs over the whole sorted frame and therefore produces globally unique ids in one pass; a per-user cumcount collides across users and forces a composite key on every downstream join.
- Build the summary with a single groupby('session_id').agg(...). user_id and account_id can be carried with 'first' only because the sort key groups them — state that dependency, since it silently breaks if someone later re-sorts the frame.
- Decide explicitly what a session means when one user_id holds memberships in several accounts: either add account_id to the sort and group keys, or document that sessions may cross accounts. Leaving it undecided produces sessions whose account_id is whichever row sorted first.
Worked solution 30 min
- human = df[df.user_id.notna() & ~df.traffic_class.isin(['ci','synthetic_monitor','load_test'])].sort_values(['user_id','request_at'], kind='mergesort').reset_index(drop=True)
- gap = human.groupby('user_id', sort=False).request_at.diff(); human['session_id'] = (gap.isna() | (gap > pd.Timedelta(minutes=30))).cumsum()
- summary = human.groupby('session_id').agg(user_id=('user_id','first'), account_id=('account_id','first'), start=('request_at','min'), end=('request_at','max'), n_requests=('request_at','size')).reset_index()
- assert summary.n_requests.sum() == len(human) and human.groupby('session_id').user_id.nunique().max() == 1
Follow-up
- Where does 30 minutes come from, and how would you pick it from this data instead of from convention?
- An engineer reused their personal key for a nightly batch job, so machine traffic carries a human user_id. How would you detect that, and should those requests form sessions?
- How much does the session count change if you sessionise before dropping CI traffic rather than after?
Find paid seats that never called the API
dim_user_membership carries user_id, account_id, seat_type, is_service_account, deactivated_at and last_seen_at. fct_api_request carries user_id, which is null whenever the caller is a service account or an unattended key, plus account_id, request_at and http_status. For a single account, list every licensed_paid seat with is_service_account = false and deactivated_at null that issued no request at all in the trailing 90 days, returning user_id and last_seen_at. A first draft writes NOT IN against a subquery over fct_api_request.user_id. State exactly what that draft returns and why, then write the correct query.
Approach
- Read the column comment first: user_id is nullable on the fact, so the subquery almost certainly contains at least one NULL for any account that runs a service account or an unattended key.
- Work the three-valued logic out loud. x NOT IN (a, NULL) expands to NOT (x = a OR x = NULL); the second disjunct is UNKNOWN, so for any x not equal to a the whole predicate is UNKNOWN and the row is filtered out. The draft returns zero rows, which reads as full seat utilisation.
- Rewrite as NOT EXISTS with the 90-day and status predicates inside the correlated subquery. Putting them in the outer WHERE instead turns the anti-join into a different question and silently changes the answer.
- Correlate on both user_id and account_id. A membership is (user_id, account_id) and one person can hold memberships in several accounts, so correlating on user_id alone marks a seat as active because that human was busy somewhere else.
- Filter the seat side to seat_type = 'licensed_paid', is_service_account = false and deactivated_at IS NULL, and say what the resulting count means next to contracted_seats on the current subscription row.
Worked solution 15 min
- Confirm the hazard with one query: SELECT count(*) FROM fct_api_request WHERE user_id IS NULL AND account_id = :account_id. Any non-zero result proves the draft returns nothing.
- Write the seat side: SELECT user_id, last_seen_at FROM dim_user_membership WHERE account_id = :account_id AND seat_type = 'licensed_paid' AND NOT is_service_account AND deactivated_at IS NULL.
- Attach AND NOT EXISTS (SELECT 1 FROM fct_api_request r WHERE r.user_id = m.user_id AND r.account_id = m.account_id AND r.request_at >= now() - interval '90 days').
- Compare the row count against the same query written as a LEFT JOIN with a WHERE r.user_id IS NULL; the two must agree exactly.
- Divide the active seat count by contracted_seats from the account's current fct_subscription_period row and state the utilisation figure.
Follow-up
- Adding AND user_id IS NOT NULL to the subquery also fixes NOT IN. Why is NOT EXISTS still the form you would leave in the repository?
- last_seen_at looks like a shortcut for the whole query. What does it actually record, and where does it disagree with API activity?
- This is a seat-reduction risk list. What threshold would you attach before handing it to an account team, and what happens to seats below it?
Running commitment burn-down and the date consumption crosses it
fct_subscription_period gives committed_amount_cents, term_start_date and term_end_date for each account's current version where pricing_model = 'committed_consumption'. fct_usage_daily gives account_id, workspace_id, sku_code, usage_date and net_amount_cents, at one row per workspace and SKU per day. Inside each account's current term, return the running total of net_amount_cents by usage_date, the first usage_date on which that running total reaches committed_amount_cents, and the fraction of the term elapsed at that point. Accounts that have not reached their commitment must still appear, with a null crossing date.
Approach
- Collapse usage to one row per (account_id, usage_date) first. The fact is grained by workspace and SKU, so a raw running total leaves several rows per date and the first-crossing date becomes dependent on the arbitrary order of rows inside that day.
- Restrict to the term with usage_date BETWEEN term_start_date AND term_end_date on the account's current row, so no prior term's consumption leaks into this term's burn-down.
- Compute SUM(net_cents) OVER (PARTITION BY account_id ORDER BY usage_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW). Name ROWS explicitly: the default frame is RANGE, which includes all peer rows at the same ORDER BY value and would hand back the whole day's total on each of that day's rows.
- Extract the crossing with MIN(usage_date) FILTER (WHERE running_cents >= committed_amount_cents) grouped per account, which returns NULL for accounts still under commitment instead of dropping them.
- Elapsed fraction is (crossing_date - term_start_date)::numeric / NULLIF(term_end_date - term_start_date, 0); Postgres date subtraction yields whole days, so the guard matters for same-day terms.
- Exclude the trailing days still inside the metering settling window, measured from first_written_at against restated_at, and say how many days you cut and why.
Follow-up
- Turn this into an end-of-term overage forecast. What breaks if you extrapolate a linear run rate on a consumption product?
- An account crosses its commitment at 40 percent of the term. Is that an expansion signal or a billing-surprise risk, and what would you check to tell them apart?
If a product metric drops unexpectedly, how do you structure your inve…
If a product metric drops unexpectedly, how do you structure your investigation to identify the root cause?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- 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.
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 between multiple data projects when resources ar…
How do you prioritize between multiple data projects when resources are constrained?
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.
- 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?
- Which segment would you cut first, and what would that rule out?
Explain how you would handle sample ratio mismatch in a product experi…
Explain how you would handle sample ratio mismatch in a product experiment.
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Decide the analysis before seeing data, including how long it runs and when you look.
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?
What statistical methods do you use to validate forecasting accuracy f…
What statistical methods do you use to validate forecasting accuracy for long-term horizons?
Approach
- State the primary metric and the minimum effect worth shipping, then size the test.
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Say whether units interfere with each other, and switch design if they do.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
How do you design an A/B test to measure the impact of a new personali…
How do you design an A/B test to measure the impact of a new personalization 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
- What would you do if you could not randomise at all?
- What would you conclude if the result is positive but the test is underpowered?
Set the operating point for a capacity-bound renewal-risk worklist
A customer-success team of six can contact at most 40 accounts a week. You have an account-level renewal-risk score and the tables behind it: fct_subscription_period (account_id, term_end_date, arr_cents, amendment_type, booked_at, is_current), fct_support_ticket (account_id, severity, reopened_count, breached_sla, linked_incident_id, opened_at) and fct_api_request. Define the metric that grades this worklist, set the operating point, and state what happens to accounts below the line. The team will not use a score they cannot explain, so say how that constrains what you build.
Approach
- Grade the list the way it is used. The team works 40 accounts a week, so the metric is precision at k = 40, not AUC. A global AUC over thousands of accounts rewards correct ordering among accounts nobody will ever call, which is ordering that cannot change an outcome.
- Restrict the candidate pool before ranking anything: accounts whose term_end_date falls within the next 90 days. An account eleven months from renewal cannot churn in the measurement window, so it can occupy a slot in the list while being structurally incapable of producing the outcome.
- Report two numbers because they disagree. Precision at 40 counts accounts; ARR captured at 40 counts money. A list that catches many small churns and misses one large one scores well on the first and badly on the second, so state which the team is optimising before the ranking is built.
- Audit leakage by timestamp, feature by feature. Every feature must carry a timestamp proving it existed before the scoring date: a downgrade amendment's booked_at, a ticket's opened_at, a churn reason code written at close. Drop anything you cannot date, and be specific that reason codes and cancellation amendments are written after the renewal conversation started.
- Attach a policy to the threshold, because a threshold without one is not a decision. Accounts ranked 41 and below get no contact this week; that is an accepted cost, and the weekly readout must include the churn that landed below the line so the operating point can be revisited rather than assumed. Favour a small inspectable model, because a stronger model the team ignores has an effect of zero.
Worked solution 30 min
- Build the weekly candidate set: accounts from the current fct_subscription_period version whose term_end_date falls within 90 days of the scoring date.
- Assemble features only from rows timestamped before the scoring date: trailing 28-day 5xx rate and successful production request volume from fct_api_request, sev1 and sev2 ticket counts plus reopened_count from fct_support_ticket filtered on opened_at, and seat utilisation from the contract version current at that date.
- Label the outcome as the absence of a successor fct_subscription_period row with amendment_type in ('renewal','upsell','downgrade') within 45 days of term_end_date.
- Backtest week by week: take the top 40, and report precision at 40, ARR captured at 40, and the churned ARR that landed below rank 40.
Follow-up
- Coverage is assigned by this list, so contacted and uncontacted accounts differ systematically in exactly the risk signal that drives the outcome. How would you ever measure whether the outreach worked?
- Annual contracts produce few churn events per quarter. At what point is the honest answer that there is not enough signal to model this, and what would you deliver instead?
Net revenue retention jumps sixteen points in one month
Trailing-twelve-month net revenue retention printed around 108 percent for months and now reads 124 percent, with no unusual deals closed. The query sums arr_cents from fct_subscription_period (account_id, arr_cents, term_start_date, term_end_date, amendment_type, superseded_by_id, is_current, booked_at) filtered on is_current = true at month M, across accounts holding arr_cents > 0 at month M-12. Find the defect, correct the number, and rewrite the definition so the next person cannot reintroduce it.
Approach
- Audit the grain before the arithmetic: count account_ids holding more than one row with is_current = true and superseded_by_id null. A versioned contract table that double counts one amendment batch inflates the numerator while leaving the denominator untouched.
- Replace is_current with an as-of selection on both dates, taking the version whose term_start_date and term_end_date bracket the reporting date and tie-breaking on latest booked_at. The numerator is read as of M and the denominator as of M-12; neither uses today's live version.
- Verify the cohort is frozen. The account set is fixed at M-12 and nothing acquired since may enter the numerator, so check that no join to a current-period table quietly re-admits new accounts.
- Confirm the estimand is a ratio of sums rather than a mean of per-account ratios. Contraction is floored at zero while expansion is unbounded, so the two constructions differ systematically and the second is far noisier.
- Reissue the definition with the failure modes written into it: exactly one row per account per date by construction, cohort frozen at M-12, churned accounts contributing zero rather than dropping out of the numerator.
Follow-up
- A churned account should contribute zero rather than disappear. What does the ratio do under each treatment, and which one is correct?
- How would you unit-test this metric so a future amendment batch with the same defect fails a check instead of reaching a board slide?
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.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Breadth 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 ↗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.
An answer without a quantity is hard to interrogate, so interviewers keep probing until they find one. Come with the baseline, the change, the window it was measured over, and how confident you were. If the effect never got measured, say so and say what you would have measured. Fabricated precision is worse than an honest gap.
How do you handle data drift and monitor model performance after deplo…
How do you handle data drift and monitor model performance after deployment?
Approach
- Close with what you would do differently, concretely.
- State the situation in two sentences and spend the rest on your reasoning.
- Pick a story where you drove the decision, not one where you observed it.
Follow-up
- How did you know the outcome was caused by your change?
- What did you decide not to do, and why?
Defend a churn number twelve times the one in the board deck
You recompute logo churn on the renewal-eligible base from fct_subscription_period, counting only accounts whose term_end_date fell in the month and allowing a 45-day grace for late paperwork. Annualised, about 14 percent of accounts that reach a renewal date do not renew. A revenue leader has been quoting 1.2 percent to the board for two quarters, computed by dividing non-renewed accounts by the entire customer base in each month and printing that monthly figure with no period attached. You have 20 minutes with that leader and the finance lead. Decide which number is reported from now on, and what happens to the two quarters already published.
Approach
- The interviewer is probing whether you can hold a correct definition under social pressure without turning it into a competence dispute. Open by reproducing their 1.2 percent exactly, with their denominator and their months, so the disagreement is arithmetic both sides can see rather than a claim about who was careless.
- Separate the two defects, because they are different in kind. The denominator is wrong: on annual contracts only about one twelfth of the base reaches a renewal date in any month, so an account eleven months from renewal sits in the denominator while being structurally incapable of entering the numerator, which suppresses the rate by a factor near twelve. The period is merely unstated: a monthly figure printed beside annual revenue targets gets read as an annual rate.
- Say out loud that those two defects nearly cancel in the level, before the leader finds it. Twelve times 1.2 percent is about 14 percent, which is your number. That is the strongest thing you can say in the room, because it proves both figures rest on the same non-renewal count and moves the meeting onto which denominator and which period get published rather than onto whose query is right.
- The level is recoverable; the series is not. Non-renewals in a month are the eligible base for that month times the churn rate, so dividing by a fixed whole base makes the published line proportional to how many contracts happen to come up that month. Where signings cluster at quarter ends, the eligible base in a quarter-end month can be several times a quiet month's, and the month-over-month moves the board has been reading as satisfaction are the signing calendar.
- Separate the measurement change from a business change. Nothing got worse this week; the loss rate was always this. Bring net revenue retention over the same period as a ratio of sums on a cohort frozen twelve months earlier, because logo churn concentrated in small accounts can sit beside healthy revenue retention, and that combination is the actual story.
- Offer a migration path rather than a correction. Report both rates for one quarter with a written bridge, restate the prior two quarters in an appendix instead of silently, and pin the definition, including the period it is stated over, somewhere finance and product both read it. Concede the limits of your own number: the 45-day grace means the most recent 45 days are not reportable, and churn must be dated on term_end_date rather than on updated_at. A strong answer volunteers this; a generic one only defends.
Follow-up
- The leader multiplies their monthly figure by twelve, lands on your annual number, and concludes nothing was ever wrong. What do you say?
- The leader says publishing the corrected rate costs the team its credibility with the board this quarter. What do you do?
- Gross logo retention worsened while net revenue retention improved. Which do you lead with, and what does the combination tell you about who is leaving?
Walk through an analysis you later discovered was wrong
Six weeks ago you reported that consumption fell 9 percent in the last week of the month, and a team spent a sprint investigating the cause. The fall was an artefact: rows in fct_usage_daily land late and are restated in place, and you queried before the tail had settled. Describe how you found the error, what you told the people who acted on it, and the control you put in place so this class of mistake cannot reach a dashboard again. Be specific about how the settling window was measured.
Approach
- The interviewer is probing whether you self-report errors before someone else finds them, and whether your fix is structural rather than a promise to be more careful. Say plainly that the number was wrong and that a sprint was spent on it, before describing any diagnosis.
- Establish the artefact quantitatively instead of asserting that data lands late. For each usage_date, compare the total as of first_written_at against the settled total and read the settling time off that curve, for example 97 percent of final by day three and 99.5 percent by day five.
- Correct the record the same day, in the channel the original number went out in, to the same audience. The cost of the wasted sprint belongs in the correction, not in a footnote.
- Make the fix structural: exclude a trailing lag window from every reportable figure, and make the reporting view return no rows inside that window rather than returning partial ones. A dashboard that shades unsettled days still gets read as a decline.
- State what generalises. Any fact table restated in place has this failure mode, so the guard belongs at the source rather than on the one dashboard that embarrassed you. A strong answer ends with the class of error closed; a generic one ends with a lesson learned.
Follow-up
- How did you choose the completeness threshold behind the lag window, and what would make you recalibrate it?
- What did you say to the team that lost the sprint, and what did they say back?
- Is there a legitimate case for showing the unsettled tail at all, and to whom?
- 01
How do you handle data drift and monitor model performance after deployment?
- 02
You recompute logo churn on the renewal-eligible base from fct_subscription_period, counting only accounts whose term_end_date fell in the month and allowing a 45-day grace for late paperwork. Annualised, about 14 percent of accounts that reach a renewal date do not renew. A revenue leader has been quoting 1.2 percent to the board for two quarters, computed by dividing non-renewed accounts by the entire customer base in each month and printing that monthly figure with no period attached. You have 20 minutes with that leader and the finance lead. Decide which number is reported from now on, and what happens to the two quarters already published.
- 03
Six weeks ago you reported that consumption fell 9 percent in the last week of the month, and a team spent a sprint investigating the cause. The fall was an artefact: rows in fct_usage_daily land late and are restated in place, and you queried before the tail had settled. Describe how you found the error, what you told the people who acted on it, and the control you put in place so this class of mistake cannot reach a dashboard again. Be specific about how the settling window was measured.
Is this an official Taglynk interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Taglynk. Rounds and questions reflect what candidates have reported, not a process Taglynk has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical interviews at Taglynk?
The technical bar is high, focusing on practical application rather than just theory. Expect to write clean, efficient code and explain the "why" behind your architectural decisions.
PracHub interview research ↗What is the typical timeline for the hiring process?
Given the "Immediate Joiner" requirement, the process is streamlined and generally moves faster than standard industry timelines. Aim to be ready for interviews within a week of your initial screen.
PracHub interview research ↗Does Taglynk offer remote or hybrid work?
The roles are typically based in Mumbai or Bangalore, often with a preference for in-office or hybrid collaboration to foster team integration.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
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