The Data Scientist role at ADCI - Karnataka is a high-impact position central to the organization's ability to turn massive datasets into actionable business intelligence. You will operate at the intersection of product strategy, statistical rigor, and machine learning, ensuring that the company’s decision-making is backed by empirical evidence. Whether you are optimizing existing workflows or designing new experimentation frameworks, your work directly influences the product features and operational efficiencies that define the user experience.
This role is both challenging and intellectually stimulating, requiring you to navigate complex data environments. You will be expected to translate ambiguous business problems into well-structured analytical tasks. Because ADCI - Karnataka operates at significant scale, your solutions must be robust, scalable, and highly interpretable. Success in this position requires not only deep technical proficiency in SQL and modeling but also the ability to communicate your findings effectively to non-technical stakeholders across the organization.
Recruiter Screen
reportedMost candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.
What to demonstrate
- Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
- Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
- The substance of the questions you ask back, which an experienced screener reads as a level signal
How to prepare
- Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
- Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
- Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered
Technical Assessments
reportedA handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.
What to demonstrate
- Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
- Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
- Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.
How to prepare
- Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
- Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
- If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows
Live-Coding Environment
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
PracHub editorial advice for the preparation topics above.
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.
Treating last-touch attribution as the causal value of a channel
The attribution label on dim_user is the output of a rule that assigns full credit to whichever touch happened to be recorded last inside a lookback window, and that rule systematically rewards channels that sit close to the conversion, especially branded search and retargeting, which largely intercept demand that already existed. Reallocating spend on those labels moves budget toward the channels that are best at being last, which is why attributed return on ad spend often improves while total signups do not. Nothing in the touchpoint data can settle this, because the counterfactual of not running the channel was never observed. The credible reads are a geo holdout or a scheduled pause, sized in advance on the total-signups metric rather than on the attributed one, and the honest framing in the meantime is that the label describes correlation with conversion and not incremental contribution.
SQL that silently fans out on a one-to-many join
State the grain of each table and the grain you want in the result before writing the join. Pre-aggregate the many side to the join key, or use EXISTS or a window function, and verify with a row count against COUNT(DISTINCT id) rather than trusting that the numbers look plausible.
Sizing estimates built on unnamed, unrevisable assumptions
Write each assumption as a named number you can change, then show the arithmetic so the interviewer can challenge one input instead of the whole answer. Finish by saying which assumption the result is most sensitive to, which matters more than the point estimate.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you approach feature selection when dealing with high-dimension…
How do you approach feature selection when dealing with high-dimensional datasets?
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Write down the assumption the method needs before you use the method.
- Say what the estimate is of, and over what population it generalises.
Follow-up
- What sample size would you need to detect an effect half this size?
- How would you explain this result to someone who does not know statistics?
What are the key assumptions behind a linear regression model and how …
What are the key assumptions behind a linear regression model and how do you test for them?
Approach
- Say how the offline result would be validated online before it is trusted.
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Check what information would not exist at prediction time, and exclude it.
Follow-up
- 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?
Cluster bootstrap for a per-session rate randomised on users
An experiment randomised on user_id reports a per-session conversion rate, so sessions inside a user are correlated. Input: one row per session with user_id, variant in {control, treatment} and converted in {0,1}. Write a cluster bootstrap from scratch: resample users with replacement within each arm, keep every session of a drawn user, recompute each arm's ratio of converted sessions to sessions, and take the difference. Return the point estimate, a 95 percent percentile interval from at least 2,000 resamples, the naive session-level interval that ignores clustering, and the ratio of their widths.
Approach
- Name the estimand precisely: it is a ratio of sums, sum(converted) over sum(sessions) within an arm, not the mean of per-user rates. Those differ whenever session counts vary across users, and the ratio is what the reported metric is.
- Resample the cluster, not the row. Draw n_users user ids with replacement inside each arm and take every session belonging to each draw, including duplicate draws of the same user. Keeping the user count fixed per arm rather than the session count is what preserves the sampling design.
- Precompute per-user (converted_sum, session_count) once, so each resample is two vector lookups and a division rather than a repeated filter over the session frame. That turns 2,000 resamples from minutes into under a second.
- Take the 2.5th and 97.5th percentiles of the 2,000 differences for the interval, and report the point estimate from the full data rather than from the bootstrap mean, since the bootstrap mean carries the resampling bias.
- Compute the naive interval from the session-level binomial standard error and compare widths. The expected inflation is roughly sqrt(1 + (m-1)*rho), with m the mean sessions per user and rho the intraclass correlation of converted within users, so a computed ratio far from that value points at a bug in one of the two intervals.
Worked solution 35 min
- per_user = df.groupby(['variant','user_id'])['converted'].agg(['sum','size']); split into two arrays per arm.
- point = (t_sum.sum() / t_n.sum()) - (c_sum.sum() / c_n.sum()).
- For b in range(B): idx = rng.integers(0, len(t_sum), len(t_sum)); ratio_t = t_sum[idx].sum() / t_n[idx].sum(); same for control; store the difference. Vectorise by drawing a (B, n) index matrix if memory allows.
- ci = np.percentile(diffs, [2.5, 97.5]); naive_se = sqrt(p_t*(1-p_t)/n_sessions_t + p_c*(1-p_c)/n_sessions_c); naive_ci = point +/- 1.96*naive_se.
- width_ratio = (ci[1]-ci[0]) / (naive_ci[1]-naive_ci[0]).
Follow-up
- Users average 3.4 sessions and the intraclass correlation is 0.12. What width ratio do you predict before running it, and does your bootstrap land there?
- Give the delta-method standard error for this ratio and say when you would prefer it to the bootstrap.
- Half the users in the treatment arm have exactly one session. What does that do to the cluster bootstrap's coverage, and how would you check it?
Explain how you would handle missing or null data points when joining …
Explain how you would handle missing or null data points when joining multiple large datasets.
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.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
Follow-up
- How would you verify this result without re-running the same query?
- What breaks if events arrive late or out of order?
Write a query using SQL window functions to calculate a rolling averag…
Write a query using SQL window functions to calculate a rolling average of user engagement.
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
How would you use a GROUP BY statement to aggregate daily active users…
How would you use a GROUP BY statement to aggregate daily active users across different regions?
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which table is the grain you start from, and join outward from it.
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?
Paying accounts with no active seat in 28 days
dim_account holds account_id, account_type, lifecycle_status, seats_licensed. fct_event holds account_id, user_id, occurred_at_utc, is_core_action, and its account_id is NULL for every signed-out and pre-signup event. Find accounts with lifecycle_status = 'active' and account_type <> 'internal' that had no distinct user complete a core action in the trailing 28 days. Return account_id, seats_licensed and days since that account's most recent core action, with NULL where the account has never emitted one. Order by seats_licensed descending.
Approach
- Build the recent-activity set first: fct_event rows with is_core_action = TRUE, occurred_at_utc >= now() - interval '28 days', and an explicit account_id IS NOT NULL. Making the NULL exclusion explicit in the CTE is what lets you reason about the anti-join afterwards.
- Express the exclusion with NOT EXISTS (correlated on account_id) or a LEFT JOIN with an IS NULL guard. Do not use NOT IN against this column: it is nullable, and SQL's three-valued logic turns the whole predicate UNKNOWN, returning zero rows.
- Compute last-seen separately as MAX(occurred_at_utc) per account over all history, LEFT JOINed on, so an account that has never emitted a core action (NULL) is distinguishable from one that went quiet six weeks ago. Those two cases have different causes and different owners.
- Rank by seats_licensed, or better by the account's current mrr_cents_constant_fx if you are allowed the subscription table, because a silent fifty-seat account is a renewal conversation and a silent one-seat account is noise.
- Before shipping, check whether the never-seen group is a cluster by signup date or surface. A block of accounts with no events at all is usually an instrumentation gap, not a set of customers who stopped using the product.
Worked solution 25 min
- Count NULL account_id rows in fct_event over the window so you know the trap is live in this data rather than hypothetical.
- Write the active-account spine and the 28-day activity CTE.
- Write the anti-join with NOT EXISTS, then deliberately run the NOT IN version and record that it returns zero rows.
- Add the all-time MAX(occurred_at_utc) LEFT JOIN and derive days_since as a date difference.
- Split the output into 'quiet' and 'never seen' and eyeball the never-seen group for a shared signup window or surface.
Follow-up
- How would you distinguish a genuinely idle account from one whose events lost their account_id after an instrumentation change?
- Would you count on fct_event.account_id or resolve user_id through dim_user instead, and what does each choice miss?
- Licensed-seat utilisation is the continuous version of this. How would you turn this boolean into that ratio?
How would you design a metric to measure the success of a new feature …
How would you design a metric to measure the success of a new feature launch?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Name one primary metric, then the guardrail that stops it being gamed.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
Describe a situation where you had to prioritize between two competing…
Describe a situation where you had to prioritize between two competing projects.
Approach
- 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.
- Decompose the metric into the rates that drive it, and say which one you would check first.
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 you notice a sudden drop in a key product metric, how would you go …
If you notice a sudden drop in a key product metric, how would you go about diagnosing the root cause?
Approach
- Fix the population and the time window before naming any metric.
- Restate the decision this analysis has to support, and who acts on the answer.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
How do you determine if an experimental result is statistically signif…
How do you determine if an experimental result is statistically significant?
Approach
- 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.
- Say whether units interfere with each other, and switch design if they do.
Follow-up
- How would you handle interference between treated and control units?
- What would you do if you could not randomise at all?
What are the common pitfalls in A/B testing, and how would you mitigat…
What are the common pitfalls in A/B testing, and how would you mitigate them?
Approach
- Say whether units interfere with each other, and switch design if they do.
- 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.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- What would you do if you could not randomise at all?
Diagnose a 50.5 to 49.5 split before reading the result
An experiment randomised on visitor_id ran for 11 days. fct_experiment_exposure holds 104,320 control rows and 102,180 treatment rows for the experiment_id, all with is_in_analysis_population = TRUE, against a configured 50/50 split. The treatment arm shows a 3.1% relative lift in visit-to-signup conversion at p = 0.004, and the team wants to ship. Decide whether that lift can be reported, show the arithmetic behind your decision, and name the three checks you would run first on the exposure logging.
Approach
- Test the split instead of eyeballing it. Chi-square goodness of fit against 103,250 expected per arm gives 2 x 1070^2 / 103,250 = 22.2 on 1 degree of freedom, p about 2.5e-6. The conventional alarm threshold is p < 0.001, so this is a sample ratio mismatch.
- Treat the mismatch as invalidating, not as a caveat. The arms are no longer exchangeable, so the 3.1% lift has no causal reading and rebalancing on covariates afterwards does not restore it.
- Walk the logging path in the order units flow through it: assignment service, then the exposure event fired on the treated surface, then the filters that build the analysis population. Each stage can drop one arm preferentially.
- Name the three suspects this stack actually has. Exposure emitted by client code the treatment arm reaches later or not at all when its bundle errors; an extra redirect or render in one arm that loses slow connections; and a post-assignment filter (is_bot_flagged, is_internal, consent_state) that correlates with variant.
- Check whether the imbalance is uniform by splitting counts on session_date, surface, app_version and device_type. A mismatch confined to one client build or to day one points at a deployment fault that can be fixed and re-run, not at a design fault.
Worked solution 15 min
- N = 206,500, expected 103,250 per arm, observed deviation 1,070, which is 0.518% of the total pushed into one arm.
- Chi-square = 2 x 1070^2 / 103,250 = 22.18 on 1 df, p about 2.5e-6.
- Compare with the p < 0.001 threshold and stop: the readout is void.
- Re-pull the same counts grouped by session_date and app_version to localise where the rows were lost.
- Deliver one line: the split is off by 1,070 visitors, a roughly one-in-400,000 event under correct randomisation, so the experiment is being fixed and re-run rather than read.
Follow-up
- The imbalance is entirely in one app_version on android. Can you salvage the other surfaces, and under what argument?
- How would you monitor this across every running experiment without drowning in false alarms from a daily chi-square at alpha 0.05?
- The missing rows turn out to be disproportionately visitors with consent_state = 'unknown'. What does that do to the estimate even after the logging bug is fixed?
A conversion rate that fell in one regulatory region
Visit-to-signup conversion fell 1.3 points over six weeks. Signups cut by dim_user.country_code put the fall in one regulatory region where a consent banner shipped in week one, but absolute signups from that region are flat. fct_session carries consent_state, visitor_id, is_bot_flagged and session_date and no country column, so the denominator cannot be cut the same way. Using fct_session and fct_event, decide whether behaviour changed or the denominator did, state what these tables cannot settle, and name the one column that would settle it.
Approach
- Name the asymmetry before computing anything. The numerator is user-keyed and therefore cuttable by country; the denominator is visitor-keyed and is not. Dividing a region-filtered numerator by an unfiltered denominator produces a quantity that is not a rate, and presenting it as a regional conversion rate is the first mistake available here.
- Attack the denominator on the dimension you do have. Compute distinct visitor_id per week and sessions per distinct visitor_id per week: a consent banner that blocks or shortens the identity cookie raises the distinct-visitor count and lowers sessions per visitor, which depresses any visitor-keyed rate with no behaviour behind it.
- Split on consent_state. Sessions with consent_state = 'denied' can enter the denominator but can never be joined forward to a signup, so a rising denied share mechanically drives the pooled rate down by roughly its own share. Report the granted-only rate and the denied share as two separate numbers rather than one blended figure.
- Cross-check with measures that do not depend on the visitor key at all: absolute weekly signups, which are given as flat, and signups per session rather than per visitor.
- State the limit honestly. Without country on the session or on its entry event, the regional attribution rests on the numerator alone, and the correct request is that one column, not a more elaborate model on top of the data you have.
Follow-up
- If granted-only conversion is the metric going forward, what selection bias have you accepted, and in which direction does it point?
- How would you handle the six weeks of already-published history once the new definition is adopted?
- What is the smallest instrumentation change that restores a cuttable denominator without collecting more personal data than before?
Roughly 90 minutes a night on weekdays with one longer weekend block. The plan deliberately cuts scope rather than compressing everything, on the assumption that finishing one thing a night beats half-starting four.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix the scope and set a baseline
- Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list for everything else and keep it visible all week.
- Take one 20-minute SQL prompt and one 10-minute metric question cold, and write the single sentence that says what blocked each attempt, since that sentence is what decides which two topics get the most evenings.
- Set the week's one rule: one problem finished to completion every night, including the night you only have 40 minutes.
Deliverable: A one-page scope with an explicit not-doing list and two cold attempts, each carrying one sentence on what blocked it.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02One query pattern, written three times
- Choose the single pattern most likely to appear (a cohort retention grid, or a funnel counted by user) and write it three times from a blank file rather than editing the previous attempt.
- On the third attempt, write the grain of every CTE as a comment before writing its body.
- Stop at 90 minutes even if the third version is imperfect, and write the one thing you would fix with another hour.
Deliverable: Three independent versions of the same query plus a note on what changed between them.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Only the statistics you will be asked to defend
- Write, in under 200 words, how you would decide whether a difference between two groups is real: the test, its assumptions, and what you would switch to when an assumption fails.
- Compute a 95 percent confidence interval for a difference in proportions by hand on realistic numbers, then write in one sentence what changes if the two samples are paired rather than independent.
- Write your answer to "what does a p-value mean", check it against a definition, and delete the version that describes it as the probability the hypothesis is true.
Deliverable: A 200-word written answer and one hand-computed interval you can reproduce under pressure.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04One case, and the assumptions holding it up
- Answer one product case aloud in 20 minutes with a recording running, then listen back with a pen and mark every claim you asserted without saying what it rested on: an assumed user behaviour, an assumed data source, an assumed baseline rate, an assumed grain.
- Pick the three assumptions the recommendation actually depends on, write how you would check each one against data, and say which one being wrong would flip the recommendation rather than merely weaken it.
- Write the four-step structure you used onto a card small enough to hold in working memory when you are nervous.
Deliverable: One recording, three load-bearing assumptions each with a written check, and a four-step structure card.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Your own work, timed
- Write a 90-second version and a four-minute version of your main project, and time both out loud rather than reading them.
- Prepare answers to the two follow-ups that always come: what you would do differently, and how you knew it worked.
- Put one number in the first sentence and be able to say exactly where that number came from and what it excludes.
Deliverable: Two timed narratives with one defensible number in the opening line.
Practice prompt ↗Practice prompt ↗06The one full rehearsal, in a longer weekend block
- Run a 60-minute mock covering query work, a case and a behavioural question in a single sitting with no breaks, because sustained attention is the thing evenings have not trained.
- Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
- Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.
Deliverable: Mock notes naming three failure moments with a specific fix written under each.
Practice prompt ↗Practice prompt ↗07Taper
- Write the 20-minute warm-up you will actually do on the morning of the interview: one query you can already write from a blank file, one metric you can define out loud, and nothing you have never seen before.
- Re-read only your own notes from this week, and open no new material.
- Write down the logistics: the tool you will be asked to work in, whether lookups are allowed, and the sentence you will use when you do not know something.
Deliverable: A one-page card holding the case structure, the project numbers, and the logistics.
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.
Share an example of a project where you failed; what did you learn and…
Share an example of a project where you failed; what did you learn and how did you pivot?
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Close with what you would do differently, concretely.
- Quantify the outcome, including what you would not claim credit for.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
Quantify your own impact without claiming the topline you touched
You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.
Approach
- Recognise what is being probed: whether you apply to your own work the causal standard you would apply to somebody else's roadmap claim. Nearly everyone who would reject 'accounts that do Y retain better' will write 'I drove a 14% increase' without noticing it is the same error with a friendlier subject.
- Sort the work by the kind of evidence it can carry. The shipped experiment is the only item with a randomised estimate, so it is the only one where an effect size is defensible, and you claim the interval rather than the point estimate.
- Claim the three flat experiments as decisions prevented and price them. Features not built, or built differently, on evidence, with the engineering weeks reallocated as the number somebody else can verify. A defensible null is a delivered decision and should be written as one.
- Claim the definition fix as correctness, not as improvement. The old figure was overstated by a specific percentage and appeared in a specific set of recurring documents; the impact is the change it produced in the forecast built on top of it, not a change in churn itself.
- Claim the dashboard on usage and displacement: distinct weekly users of it, and the ad-hoc request count for six months before against six months after. If the request log does not exist, record the claim as unverified rather than estimating it upward.
- Disclaim the 14% explicitly and once. State that it cannot be separated from seasonality, other teams' launches and a pricing change, and bound your own contribution from above using the shipped experiment's interval converted into headline units.
Follow-up
- Your shipped experiment's interval was +0.2pp to +1.4pp on activation. How much of the 14% can that account for, and how do you say so without undercutting yourself?
- A peer in the same cycle claims the full 14%. What, if anything, do you do about it?
- If you could only keep two of your three claims, which do you drop, and why that one?
Choose between three teams' requests with one analyst-week
You have one analyst-week. Three requests land the same morning. A growth team wants a paid-channel readout before a Friday spend decision. A billing team wants gross monthly revenue churn rebuilt, because the current figure recognises cancellation at canceled_at_utc rather than period_end_utc and is therefore wrong. A product team wants a dashboard for a feature launching in six weeks. All three sponsors are peers of your manager. Deliver your ranking, the explicit rule that produced it, and the message you send to the two teams you defer.
Approach
- Recognise what is being probed: whether you prioritise on decision value and reversibility or on who asked most recently and most loudly. The generic answer sorts by importance; the strong one states a rule, applies it, and accepts the ranking it produces even where that is uncomfortable.
- Score each request on three statable things: the decision it unblocks and the date that decision is made, the cost of being wrong in the meantime, and whether the work is one-off or compounding. A wrong published churn figure compounds, because it is quoted downstream and enters forecasts; the channel readout has a fixed date that cannot move; the dashboard has six weeks of slack.
- Notice the tension between value and urgency rather than resolving it by feel. The churn defect is the most valuable item and the least urgent one, which is exactly the shape of work that never gets done.
- Break the churn item in two. A one-hour severity check, sizing the gap between the two recognition points in MRR, is cheap enough to do before ranking anything and may promote the item outright. Do that first, then rank.
- Make the deferrals concrete. Each deferred team gets a date, a reason expressed as another team's decision deadline rather than as relative importance, and the smallest thing you can hand them immediately.
Follow-up
- The dashboard team escalates to your manager. What do you say in that conversation?
- Your severity check shows churn is overstated by 15%. Does the ranking change, and does anybody need to be told today regardless of the ranking?
- A fourth request arrives Wednesday with a Thursday deadline. What comes off the list, and who do you tell first?
- 01
Share an example of a project where you failed; what did you learn and how did you pivot?
- 02
You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.
- 03
You have one analyst-week. Three requests land the same morning. A growth team wants a paid-channel readout before a Friday spend decision. A billing team wants gross monthly revenue churn rebuilt, because the current figure recognises cancellation at canceled_at_utc rather than period_end_utc and is therefore wrong. A product team wants a dashboard for a feature launching in six weeks. All three sponsors are peers of your manager. Deliver your ranking, the explicit rule that produced it, and the message you send to the two teams you defer.
Is this an official ADCI - Karnataka interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at ADCI - Karnataka. Rounds and questions reflect what candidates have reported, not a process ADCI - Karnataka has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How much time should I dedicate to preparing for the behavioral rounds?
Treat the behavioral portion with the same level of seriousness as the technical rounds. Prepare 5–7 stories that highlight your leadership, problem-solving, and conflict-resolution skills.
PracHub interview research ↗Is the technical interview focused more on theory or practical application?
It is heavily focused on practical application. Be ready to write code and solve real-world data problems rather than reciting textbook definitions.
PracHub interview research ↗What is the best way to demonstrate "Product Sense"?
Always frame your answers within the context of the user. Before jumping into a technical solution, clarify the business goal and the user journey involved in the problem.
PracHub interview research ↗How long does the entire interview process usually take?
While it varies, the process generally spans 4–8 weeks from the initial screening to the final decision.
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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