A Data Scientist at Genpact plays a pivotal role in driving digital transformation, operational efficiency, and advanced analytics solutions for enterprise clients worldwide. Genpact is a global professional services firm deeply embedded in industry operations, meaning that data science here is not purely theoretical. Instead, it is highly applied, focusing on translating massive, complex datasets into actionable business strategies and automated systems.
In this role, you will design, build, and deploy machine learning models, natural language processing (NLP) pipelines, and generative AI (GenAI) solutions. You will work on optimizing workflows across diverse industries such as finance, healthcare, supply chain, and retail. Your models will directly impact client decision-making, helping automate high-volume processes, predict market trends, and extract structured insights from unstructured data.
What makes this position critical and highly rewarding is the sheer scale and variety of data you will handle. You will collaborate closely with cross-functional teams, including data engineering, product management, and business operations, ensuring that the AI solutions you build are seamlessly integrated into production-grade systems. Successful candidates are those who possess both technical depth and the consultative ability to explain complex algorithmic decisions to senior business stakeholders.
Online Technical Assessment
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
Deep-Dive Technical Rounds
reportedMuch of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.
What to demonstrate
- Whether the query you write matches the plan you just described
- What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
- Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly
How to prepare
- Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
- Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
- Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock
Leadership Conversations
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.
PracHub editorial advice for the preparation topics above.
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.
Randomising an experiment at the user level when users share an account
Two problems fire at once. Colleagues in one workspace see each other's work and talk to each other, so a treated user changes the behaviour of a control user in the same account, which violates the no-interference assumption and biases the estimate toward zero. Separately, outcomes within an account are strongly correlated, so the effective sample size is roughly n / (1 + (m - 1) * rho) for m users per account and intra-class correlation rho, not n. With rho around 0.3 and twenty users per account that is a design effect near 6.7, meaning a user-level confidence interval is about two and a half times narrower than it should be and results cross significance thresholds on noise alone. Randomise the account and cluster the standard errors.
Averaging per-user rates to produce a population rate
Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.
Stopping an experiment the moment it crosses significance
Fix the sample size or duration before launch, or use a method built for continuous monitoring such as a sequential test, always-valid confidence intervals, or group-sequential boundaries. Repeatedly checking a fixed-horizon p-value against 0.05 pushes the real false-positive rate well above 5 percent.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Describe a situation where you faced an unexpected roadblock during mo…
Describe a situation where you faced an unexpected roadblock during model deployment and how you resolved it.
Approach
- 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.
- Set a baseline first, so any model has something honest to beat.
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?
Describe a project where you implemented an NLP pipeline. What preproc…
Describe a project where you implemented an NLP pipeline. What preprocessing steps did you take?
Approach
- Check what information would not exist at prediction time, and exclude it.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- 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?
- Where could label leakage enter this setup?
Simulate how the renewal calendar distorts monthly churn rates
Simulate 1,200 accounts on annual contracts. Draw each account's renewal month from a deliberately lumpy calendar: 30% renew in January and the remaining 70% are spread evenly over the other eleven months. At each renewal an account churns with probability 0.18, independent of month; survivors renew and come back twelve months later. Run 24 simulated months. For each month compute two rates: churned accounts over all live accounts, and churned accounts over accounts whose term ended that month. Report the mean and the month-to-month standard deviation of each series, and state which one belongs in an executive summary.
Approach
- Build the panel with numpy state arrays rather than a per-account loop: a next_renewal_month vector, an alive boolean vector, and a loop over the 24 months only. Looping over 24 months is fine; looping over 1,200 accounts inside it is what makes the simulation too slow to iterate on.
- Maintain the live set honestly. An account that churned in month m must leave the denominator from m+1 onward and can never be renewal-eligible again; if it stays, the naive rate drifts downward for reasons that have nothing to do with churn and the calendar effect gets buried.
- Compute both series over the same months and compare dispersion, not only level. The eligible-base rate should sit near 0.18 with binomial noise scaled by that month's renewal count; the naive rate spikes in January and collapses in thin months.
- Quantify the gap instead of describing it: the ratio of the two means is roughly the reciprocal of the average monthly renewal-eligible fraction, and the naive series' standard deviation is driven by the signing calendar rather than by customer behaviour.
- Check against the closed form before trusting the output. With the live set maintained correctly the eligible-base rate is an unbiased estimator of 0.18 in every month, so a systematic offset means the bookkeeping is wrong, not that the simulation found something.
Worked solution 30 min
- rng = np.random.default_rng(0); p = [0.30] + [0.70/11]*11; next_renewal = rng.choice(12, size=1200, p=p); alive = np.ones(1200, bool)
- For m in range(24): eligible = alive & (next_renewal == m); churn = eligible & (rng.random(1200) < 0.18); record churn.sum(), eligible.sum(), alive.sum() at month start; alive &= ~churn; next_renewal[eligible & ~churn] += 12
- naive = churned / live_at_start; eligible_rate = churned / eligible, left as NaN where eligible == 0.
- Report naive.mean(), naive.std(ddof=1), np.nanmean(eligible_rate), np.nanstd(eligible_rate, ddof=1) and the ratio of the two means.
Follow-up
- Compounded over twelve months the naive rate lands close to the true annual churn. Does that rescue it?
- How would you report churn in a month where only nine accounts were renewal-eligible?
- Eighteen-month terms are now being sold alongside annual ones. What breaks?
Explain the difference between an inner join, left join, and outer joi…
Explain the difference between an inner join, left join, and outer join in SQL, and write a query to find duplicate records in a table.
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- 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.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
Write a Python function to find the first non-repeating character in a…
Write a Python function to find the first non-repeating character in a string.
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.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
How do you optimize a slow-performing SQL query when dealing with larg…
How do you optimize a slow-performing SQL query when dealing with large-scale enterprise databases?
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.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- How would you verify this result without re-running the same query?
- What breaks if events arrive late or out of order?
Net revenue retention on a cohort frozen twelve months back
fct_subscription_period carries subscription_period_id, account_id, arr_cents, plan_code, term_start_date, term_end_date, booked_at, amendment_type, superseded_by_id (the subscription_period_id of the version that replaced this one, null on the live version of a lineage) and is_current. Compute net revenue retention for month M: the summed arr_cents at M for the set of accounts holding arr_cents > 0 at M-12, divided by that same set's arr_cents at M-12. An account can hold more than one live subscription, a churned account contributes zero rather than dropping out, and nothing signed after M-12 may enter either side. Return the ratio plus the expansion, contraction and churn components in cents.
Approach
- Write one reusable as-of ARR snapshot parameterised by a date: rows whose term brackets the date AND whose booked_at is at or before the date, then only the version of each lineage that is still live at that date, then sum arr_cents per account. The booked_at guard matters because an amendment signed in advance otherwise co-exists with the term it replaces and double counts the account.
- Collapse the lineage on superseded_by_id, not on any attribute of the contract. Keep a row when superseded_by_id IS NULL, or when the successor it points at was booked after the date. Ranking with ROW_NUMBER() OVER (PARTITION BY account_id, plan_code ...) instead is wrong in both directions: an amendment that moves the account from one plan_code to another puts the old and new versions in different partitions, so both are rank 1, both bracket the date, and the account's arr_cents is counted twice; and two genuinely concurrent subscriptions that happen to share a plan_code land in one partition, so one of them is deleted.
- Resolve the successor with a LEFT JOIN back to fct_subscription_period on subscription_period_id, and treat a missing successor as not superseded. An inner join would silently delete an account's ARR on a dangling pointer, which is a data-quality bug in the source, not a retention movement.
- Never use is_current for the M-12 side. is_current describes today; using it at the historical snapshot backdates the present contract onto last year's cohort and makes retention look like 100 percent by construction.
- Freeze the cohort from the M-12 snapshot where arr_cents > 0, then LEFT JOIN the M snapshot onto it and COALESCE the missing side to zero. An inner join deletes exactly the churned accounts, which is the single largest way this number gets overstated.
- Return a ratio of sums, not a mean of per-account ratios. The two are different estimands: contraction is floored at zero while expansion is unbounded, so the mean of ratios is both biased relative to the aggregate and far noisier on a skewed revenue base.
- Decompose per account on the delta: positive delta is expansion, negative delta with a non-zero M value is contraction, a zero M value is churn. The three components must reconcile to numerator minus denominator.
- Prove no leakage: any account whose first contract began after M-12 must be absent from both sides, and the cohort row count must be identical in the numerator and denominator.
Worked solution 45 min
- Write arr_asof(d) as a CTE or lateral: from fct_subscription_period s take rows with term_start_date <= d AND term_end_date >= d AND booked_at <= d, LEFT JOIN fct_subscription_period succ ON succ.subscription_period_id = s.superseded_by_id, keep the row when s.superseded_by_id IS NULL OR succ.subscription_period_id IS NULL OR succ.booked_at > d, then sum arr_cents per account_id across every surviving version.
- Sanity-check the lineage rule on one amended account before going further: at a date after the amendment, the account must contribute exactly one version per lineage even when the amendment changed plan_code, term dates or both.
- Materialise base = arr_asof(M-12) filtered to arr_cents > 0, and curr = arr_asof(M).
- LEFT JOIN curr onto base on account_id and COALESCE(curr.arr_cents, 0) AS arr_now.
- Compute nrr = sum(arr_now)::numeric / NULLIF(sum(base.arr_cents), 0), and the three components with SUM(...) FILTER on the sign of arr_now - base.arr_cents and on arr_now = 0.
- Reconcile: assert sum(arr_now) - sum(base.arr_cents) = expansion - contraction - churn, and assert the cohort account count is identical on both sides.
Follow-up
- Net revenue retention can rise while the business shrinks. Show one mechanism and name the guardrail that catches it.
- How do you handle an account that co-terms two subscriptions into one mid-window, so the subscription count changes but the money does not?
- Finance computes this from invoiced amounts and gets a different number. Which is right for which question?
How do your technical achievements and metrics compare to those of you…
How do your technical achievements and metrics compare to those of your peers in your previous roles?
Approach
- 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.
- 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?
- Which segment would you cut first, and what would that rule out?
Set a stopping rule for a daily-read experiment dashboard
An experiment dashboard recomputes a two-sided t-test every morning for 21 days across one primary metric and eleven guardrails. The team ships whenever anything crosses p < 0.05 and stops the test that day. Quantify the false-positive rate this procedure actually carries, from repeated looks and from the twelve metrics separately, then specify the readout protocol you would put in its place: what is checked daily, what is checked at pre-declared points, what controls the family-wise or false-discovery rate, and what the stopping rule is.
Approach
- Separate the two inflations, since they compound and have different remedies. Repeated fixed-horizon testing on accumulating data: at roughly 20 equally spaced looks with nominal alpha 0.05, the probability of crossing at least once under the null is about 0.25, and about 0.19 at 10 looks (Armitage, McPherson and Rowe, 1969). Multiplicity: twelve independent metrics at 0.05 give 1 - 0.95^12 = 0.46 of at least one false positive, somewhat lower under correlation but not much lower when metrics share a denominator.
- Point out that the stopping rule is worse than either figure alone. Stopping the moment anything crosses is optional stopping on the maximum over twelve metrics and twenty-one days, so the two inflations multiply rather than average, and the realised error rate is above 0.5 for a null effect.
- Rebuild around one pre-registered primary metric that alone decides ship or no-ship. Secondary metrics are demoted to hypothesis generation, reported with intervals and no significance marks, and are not permitted to trigger a ship on their own under any circumstances.
- For the primary, choose deliberately between two valid options and say what each costs. An alpha-spending boundary with a small number of pre-declared interim analyses, where an O'Brien-Fleming shape spends very little alpha early so the final look retains nearly the full amount; or an always-valid confidence sequence, which permits unlimited looks at the price of a wider interval at every fixed time, typically requiring on the order of 1.2 to 1.5 times the sample to match the fixed-horizon width. Whichever is chosen goes into the power calculation before launch, not after.
- Control the guardrail family separately from the primary. Benjamini-Hochberg at q = 0.10 when a missed regression costs more than an occasional false alarm; Holm when any single false positive triggers an expensive rollback. Say which regime you are in rather than reaching for a correction by habit.
- Distinguish diagnostics from readouts and write both lists down. Sample ratio mismatch, exposure counts, error rates and pipeline health are checked every single day and are not peeking, because their only permitted action is to abort the test, never to declare a win. The abort conditions belong next to the stopping rule in the same document.
Worked solution 25 min
- Record the repeated-testing inflation for 21 daily looks against the nominal 0.05, about 0.25 at 20 looks, either from published tables or by simulating a random walk under the null.
- Compute the independence bound 1 - 0.95^12 = 0.46, then re-estimate by simulation using the observed correlation matrix of the twelve metrics.
- Pick the sequential method and size the test for it, adding the confidence-sequence width penalty to the power calculation or placing two interim looks under an O'Brien-Fleming boundary.
- Write the one-page protocol: primary metric, stopping rule, interim dates, guardrail family and its FDR level, abort conditions, and the list of daily diagnostics that explicitly cannot trigger a ship.
Follow-up
- Your confidence sequence never crosses in 21 days, but the point estimate is exactly the size you powered for. What do you tell the team, and what do you do next?
- The eleven guardrails are heavily correlated with each other. Does that make Benjamini-Hochberg conservative or anti-conservative here, and does it change your q?
- Someone asks for a directional read on day five. What can you honestly give them without compromising the stopping rule?
Weekly active organisations fell nine percent over one week
A dashboard reports the weekly active organisation ratio on a trailing seven-day window ending each Wednesday. This week it reads nine percent below last week. You have fct_api_request (account_id, environment, traffic_class, http_status, request_at) and dim_account (account_id, billing_country, account_status, is_internal, is_current). Nothing was released. Decide whether usage actually fell, and hand back a corrected series plus a one-paragraph explanation that a non-analyst can repeat without you in the room.
Approach
- Count the holiday-free business days inside each window before comparing them. A trailing seven-day window spans exactly five weekdays wherever it ends, so its business-day count can only fall to four or fewer when a public holiday lands inside it and can never reach six, while usage in this domain follows a hard five-to-two weekday cycle. Two windows holding different numbers of business days are not comparable whatever the product did.
- Count distinct active accounts per calendar day for the last ten weeks and overlay the two windows. A calendar problem shows as a small number of weekdays sitting at weekend level, not as every day being uniformly lower.
- Cut the daily series by billing_country and index each country-day to that country's trailing same-weekday median, which isolates a regional public holiday from a product change.
- Check the denominator on its own: the metric divides by accounts whose account_status was in trial, free or active_paid for the whole week, so a batch suspension or status backfill moves the ratio with no change in the numerator at all.
- Report the series with each window's business-day count and the holiday dates annotated beside it, and state the residual week-over-week change that survives once the calendar effect is removed. Compare against earlier windows holding the same number of business days rather than dividing by business days, since distinct-account counts are sublinear in window length and dividing would over-correct.
Follow-up
- Distinct account counts are sublinear in the number of days in the window. Why does losing one of five business days reduce the count by noticeably less than twenty percent?
- How would you make this metric comparable across countries with different holiday calendars without hand-maintaining a holiday table forever?
For someone who has spent the last year in notebooks, dashboards or modelling work and has not written raw SQL under time pressure. The first four days rebuild query fluency against a fixture you control and can verify by hand; the last three attach that fluency to the rest of the loop.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Build a fixture you can check answers against
- Create a local Postgres or SQLite database with four tables (users, sessions, events, orders) holding roughly 200 rows you generated yourself, so you know the contents well enough to predict every result.
- Deliberately seed the cases that break queries: a user with no sessions, a session with no events, two orders sharing a timestamp, a NULL in one join key, and one duplicated user row.
- Before writing any SQL, hand-compute five answers on paper (how many users placed at least one order, median orders per ordering user, and three others) and save them as the ground truth for the week.
Deliverable: A one-command seed script plus a text file of five hand-computed answers to grade every later query against.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Joins, filters and NULL semantics
- Answer "which users have no orders" three ways (LEFT JOIN with IS NULL, NOT EXISTS, NOT IN) and confirm that the NOT IN version returns zero rows once the subquery contains a NULL, because the comparison is never TRUE.
- Reproduce the LEFT JOIN that silently collapses to an inner join by putting a right-table predicate in WHERE, then fix it by moving the predicate into the ON clause, and record both row counts.
- Create a fan-out bug on purpose by joining orders to order_items and summing the order total, then correct it with a pre-aggregated subquery and explain in one line which table changed the grain.
Deliverable: One annotated .sql file holding the three join traps, each with the wrong result and the corrected result side by side.
Practice prompt ↗Practice prompt ↗03Window functions and frames
- Write three window queries against the fixture: a running order total per user, the rank of each order within its user by value, and the day gap to that user's previous order, then check each against the day-one ground truth.
- Run ROW_NUMBER, RANK and DENSE_RANK over a column containing ties, print all three side by side, and write one sentence on when each is the correct choice.
- Switch one query from the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is what you get when ORDER BY is present and no frame is written) to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and explain why the output differs only when the ORDER BY column has duplicates.
Deliverable: Three verified window queries plus a short note explaining the RANGE versus ROWS difference in your own words.
Practice prompt ↗Practice prompt ↗04The four analytical query patterns
- Write a monthly retention grid: first order month per user, then months-since-first as the column, and verify that month zero equals the cohort size exactly.
- Sessionize the events table under a 30-minute inactivity rule using LAG plus a cumulative sum over a new-session flag.
- Build a four-step funnel that counts distinct users rather than events at each step, and state the rule you applied to a user who reaches step three without ever logging step two.
Deliverable: One file with the retention, sessionization and funnel patterns, each carrying a one-line note on the assumption it bakes in.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Write SQL the way you will have to write it live
- Set a 12-minute timer and solve three medium prompts in a plain editor with no execution and no autocomplete, then run them and tally syntax errors separately from logic errors.
- Narrate one solution aloud while writing it, stating the grain of each intermediate result (one row per user, one row per user-day) before you type its body.
- Rewrite your slowest solution as a CTE chain where every CTE name states its grain, and time yourself re-solving it from blank.
Deliverable: A recording of one narrated solution plus an error tally that separates syntax from logic.
Practice prompt ↗Practice prompt ↗06One day for everything that is not SQL
- Write the preconditions of the two-sample t-test from memory, then check them: independent observations, and a difference in means whose sampling distribution is approximately normal, which at large sample sizes follows from the central limit theorem rather than from normality of the raw values.
- Write the difference between an odds ratio from logistic regression and a relative risk, and state the condition under which the two are close (low outcome prevalence).
- Prepare a 90-second answer to "how would you know this model is any good" that names the metric, the baseline you would beat, and the cost of the errors you care about.
Deliverable: One page of notes covering test preconditions, the odds-ratio caveat and the model-quality answer.
Practice prompt ↗Practice prompt ↗07Full loop rehearsal
- Run a 45-minute mock with someone willing to interrupt: 20 minutes of SQL, 15 minutes defining a metric, 10 minutes on a past project.
- Re-solve from blank the two queries you were slowest on this week and compare the times against day five.
- Write a five-line answer to "walk me through a project" that puts a number in the first sentence and names the decision the work changed.
Deliverable: Mock feedback notes plus a timed project narrative you can deliver without reading it.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Half of this section is about translation. Be ready to describe how you explained a result to someone who did not want the method, only the implication, and what you did when the simplified version started being repeated in a way that overstated it. Correcting your own simplification is a strong beat.
Tell me about a time you had to explain a complex machine learning mod…
Tell me about a time you had to explain a complex machine learning model to a stakeholder or manager who did not have a technical background in AI/ML.
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Name the disagreement or constraint, and how you resolved it with evidence.
- 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 would you do differently if you ran that project again?
Walk me through your professional experiences and key projects from 20…
Walk me through your professional experiences and key projects from 2015 to the present.
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- Pick a story where you drove the decision, not one where you observed it.
- 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?
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?
- 01
Tell me about a time you had to explain a complex machine learning model to a stakeholder or manager who did not have a technical background in AI/ML.
- 02
Walk me through your professional experiences and key projects from 2015 to the present.
- 03
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.
Is this an official Genpact interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Genpact. Rounds and questions reflect what candidates have reported, not a process Genpact has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How technical is the interview process for Data Scientists at Genpact?
The process is moderately technical. While you must understand the math and theory behind machine learning models, there is a strong emphasis on practical application, basic coding, database querying (SQL), and your ability to explain your past projects clearly.
PracHub interview research ↗What is the expectation for the round with the Vice President (VP)?
The VP round focuses on your high-level problem-solving approach, your past achievements, and your business acumen. Be prepared for the possibility that some VPs may have a business background rather than a deep technical AI background; you must be able to explain your work without relying heavily on technical jargon.
PracHub interview research ↗How long does the hiring process typically take?
While the interview rounds themselves can be scheduled relatively quickly, candidates have reported that the post-interview HR compliance and offer generation stages can take anywhere from a couple of weeks to over a month. Proactive follow-up with your recruiter is highly recommended.
PracHub interview research ↗Does Genpact hire freshers for Data Scientist roles?
Yes, Genpact actively recruits freshers through campus placement drives and virtual recruitment initiatives. For freshers, the interviews focus heavily on academic projects, Python basics, standard machine learning algorithms, and foundational computer science topics.
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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