A Data Scientist at Infosys plays a pivotal role in driving digital transformation and enabling data-driven decision-making for some of the world's largest enterprises. Operating at the intersection of advanced analytics, artificial intelligence, and strategic consulting, you will translate complex business challenges into scalable machine learning solutions. The scale and diversity of clients at Infosys mean that your work will directly impact industries ranging from financial services and healthcare to retail and manufacturing.
In this role, you are not just a model builder; you are a strategic partner. You will design, develop, and deploy end-to-end machine learning pipelines, with an increasing emphasis on Generative AI (Gen AI) and large language models to meet modern client demands. The solutions you create must be robust, production-ready, and capable of handling massive enterprise datasets.
What makes this position exceptionally compelling is the sheer variety of challenges. You will collaborate with cross-functional teams of software engineers, data engineers, and business consultants to take models from conceptualization to production. At, a must balance deep technical expertise with the ability to communicate complex architectural trade-offs to non-technical stakeholders and external clients.
Initial Screening
reportedWhoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.
What to demonstrate
- Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
- Whether your reason for wanting the role points at the work itself rather than the company's reputation
- Whether your language signals the level being screened for: what you decided yourself versus what you were handed
How to prepare
- Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
- Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
- Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands
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
Client Round
reportedRounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.
What to demonstrate
- Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
- The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
- Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options
How to prepare
- Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
- Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
- Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.
HR Discussion
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.
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.
Reading consumption metrics before the metering lag window has closed
Usage pipelines land late and correct themselves, which is exactly what is_restated and restated_at record. A dashboard queried on day T sees a partially populated tail for the last several days, so the most recent points always slope downward and always look like a regression. Analysts then explain the artefact, and sometimes ship a change to fix it. Establish the empirical settling time by measuring how much a given usage_date's total moves between first_written_at and its final value, exclude that many trailing days from every reportable figure, and never compare a fresh period against a settled one.
Reporting a p-value with no effect size or interval
Give the estimated difference with a confidence interval in the units the business cares about, then say whether that whole interval is worth acting on. A p-value only addresses whether you can rule out exactly zero; it says nothing about magnitude.
Over-explaining the method and under-explaining the implication
Lead with the answer and what you would do about it, then give the approach when asked. Roughly one sentence of method per three of implication is the right ratio for a stakeholder-facing answer; the interviewer already knows what a regression is.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Describe a time when you had to explain a complex machine learning mod…
Describe a time when you had to explain a complex machine learning model to a non-technical client. How did you structure your presentation?
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Say how the offline result would be validated online before it is trusted.
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?
How would you design a deployment and monitoring pipeline to track mod…
How would you design a deployment and monitoring pipeline to track model drift in production?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Check what information would not exist at prediction time, and exclude it.
- Say how the offline result would be validated online before it is trusted.
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?
Audit daily usage rows for grain and arithmetic violations
You are handed fct_usage_daily as a pandas DataFrame with account_id, workspace_id, sku_code, usage_date, billable_quantity, included_quantity_applied, overage_quantity, list_amount_cents, discount_amount_cents, net_amount_cents, cogs_cents, is_restated, first_written_at and restated_at. The declared grain is one row per (account_id, workspace_id, sku_code, usage_date). Write audit(df) returning a DataFrame with one row per failing check: check name, failing row count, and one example key. Cover at minimum grain duplication, negative quantities or amounts, the identity net = list - discount, billable = included + overage, and rows where is_restated is true but restated_at is null.
Approach
- Check the grain before anything else with df.duplicated(subset=key, keep=False), and count rows rather than groups so a key appearing twice contributes 2 — if the grain is broken every arithmetic count below it is uninterpretable.
- Express each invariant as a boolean Series over the whole frame. The cent columns are integers and compare exactly, so use !=; the numeric(18,6) quantity columns need np.isclose with atol=1e-6 because included + overage is a decimal sum.
- Handle null as its own failure mode. Comparisons against NaN return False, so a check written as rows_that_pass = (a == b - c) silently files every null-amount row wherever the negation happens to land; build each check as violations = ~condition | column.isna().
- Collect the checks as a list of (name, mask) pairs and assemble the output in one pass, so adding a check is one line and every check reports in the same shape.
- Order the output with structural failures (grain, null keys) above arithmetic failures, and report zero-count checks too — a check that silently disappears when it passes is indistinguishable from a check that was never run.
Worked solution 20 min
- key = ['account_id','workspace_id','sku_code','usage_date']; dup = df.duplicated(key, keep=False); record dup.sum() and df.loc[dup, key].iloc[0].to_dict() as the example.
- neg = (df[['billable_quantity','included_quantity_applied','overage_quantity','list_amount_cents','net_amount_cents','cogs_cents']] < 0).any(axis=1); net_bad = df.net_amount_cents.isna() | (df.net_amount_cents != df.list_amount_cents - df.discount_amount_cents).
- qty_bad = ~np.isclose(df.billable_quantity, df.included_quantity_applied + df.overage_quantity, atol=1e-6) | df.billable_quantity.isna(); restated_bad = df.is_restated & df.restated_at.isna().
- Assemble pd.DataFrame([{'check': n, 'failing_rows': int(m.sum()), 'example': first_key(m)} for n, m in checks]) with the grain and null checks listed first.
Follow-up
- Which of these should block a dashboard refresh and which should only warn?
- Rows with is_restated = true legitimately change value after first write. How do you make yesterday's audit result reproducible?
- How would you extend this to catch a partition that is missing entirely rather than wrong?
Explain how you handle missing values and outliers during the data pre…
Explain how you handle missing values and outliers during the data preprocessing phase.
Approach
- 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.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- How does the query change if the join becomes one-to-many?
- What breaks if events arrive late or out of order?
What are Window Functions in SQL? Provide an example of how you would …
What are Window Functions in SQL? Provide an example of how you would use DENSE_RANK to analyze user activity.
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
Write a SQL query using Common Table Expressions (CTEs) to find the se…
Write a SQL query using Common Table Expressions (CTEs) to find the second-highest transaction value for each customer.
Approach
- 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.
- 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?
- How does the query change if the join becomes one-to-many?
How do you optimize a slow-running query that involves multiple joins …
How do you optimize a slow-running query that involves multiple joins on tables containing millions of rows?
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
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
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.
Worked solution 30 min
- Build terms: SELECT account_id, committed_amount_cents, term_start_date, term_end_date FROM fct_subscription_period WHERE is_current AND pricing_model = 'committed_consumption' AND committed_amount_cents IS NOT NULL.
- Build daily: join fct_usage_daily to terms on account_id with usage_date inside the term, then GROUP BY account_id, usage_date summing net_amount_cents.
- Add running_cents with SUM(...) OVER (PARTITION BY account_id ORDER BY usage_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW).
- Aggregate per account with MIN(usage_date) FILTER (WHERE running_cents >= committed_amount_cents) AS crossing_date, then compute the elapsed fraction with the NULLIF guard.
- LEFT JOIN that summary back onto terms so every committed account appears, and verify the last running_cents per account equals a plain SUM over the term.
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?
Measure named coverage at an assignment threshold you cannot randomise
Named customer-success coverage is assigned to accounts whose arr_cents at fiscal-year start is at least $50,000. About 88% of accounts above the line receive a named owner and about 9% below it do, through documented exceptions. Roughly 200 accounts sit within $15,000 of the cutoff, and gross logo retention on the renewal-eligible base runs near 0.88. Estimate the effect of coverage on renewal, name the design, state its identifying assumptions and the test for each, and say whether this data can answer the question at all.
Approach
- Say first why the naive comparison is unusable, because the answer turns on it. Coverage is assigned to the largest accounts because they are valuable and to distressed accounts because they are at risk, so selection runs in both directions at once and the naive estimate can come out with either sign depending on which rule dominated. Matching on observed size does not fix it: the risk signal that triggered coverage is the same signal that predicts renewal.
- Name the design and the estimand together. This is a fuzzy regression discontinuity with the $50,000 threshold as an instrument for coverage. The estimator is the Wald ratio, the jump in renewal at the cutoff divided by the jump in coverage probability of 0.88 - 0.09 = 0.79, and the estimand is a local average treatment effect for accounts near $50,000 whose coverage is actually decided by the rule, not the average effect of coverage across the book.
- Fit it as local linear regression on each side with a triangular kernel and a data-driven bandwidth (Imbens-Kalyanaraman, or Calonico-Cattaneo-Titiunik with bias-corrected robust intervals). A global polynomial fitted across the whole ARR range is not a substitute and is well documented to manufacture spurious jumps at interior points.
- Test the assumptions instead of asserting them. Run a density test for bunching just above $50,000, which is exactly what a sales team structuring a deal at $50,001 to win coverage would produce and which invalidates the design outright. Check continuity at the cutoff of employee_band, acquisition_channel, deployment_model and pre-period usage. Run placebo cutoffs at other ARR values, and plot the estimate against bandwidth.
- Do the power arithmetic before promising an answer. With about 100 accounts per side and a renewal base rate of 0.88 (standard deviation 0.325), the sharp-design MDE at 80% power and two-sided 0.05 is 2.80 * 0.325 * sqrt(2/100) = about 12.9 percentage points, and dividing by the 0.79 first stage gives about 16 points on the fuzzy estimand. No plausible effect of assigning an account manager is that large.
- Give the honest verdict with a route forward attached. This design cannot answer the question on one fiscal year. Pool several years at the same cutoff, accepting the added assumption that the coverage model and the threshold's meaning were stable across them, or ask for a randomised holdout among accounts near the threshold, which is cheap to justify precisely because the rule is already arbitrary there.
Worked solution 45 min
- Plot the density of arr_cents in bins around $50,000 and run the manipulation test; stop and report the design as invalid if bunching is present.
- Plot the first stage, share of accounts with a named owner against ARR, and confirm the jump is close to 0.79 and locally flat on each side.
- Fit local linear regressions of renewal on ARR either side of the cutoff with a triangular kernel and data-driven bandwidth, take the Wald ratio, and report bias-corrected robust intervals.
- Run covariate-continuity checks, placebo cutoffs, and a bandwidth-sensitivity curve for the point estimate.
- Compute the MDE at the realised effective sample and write the verdict, powered or not, with the alternative design named.
Follow-up
- The density test shows clear bunching just above $50,000. Is any part of the design salvageable, and what would you report instead?
- Coverage also correlates with support ticket severity. Does adding severity as a control help, hurt, or do nothing in a regression discontinuity?
- If you pool five fiscal years to gain power, what new identifying assumption have you taken on, and how would you check it?
Pick the randomisation unit when workspaces and queues are shared
A collaboration feature was tested by randomising 18,000 users 50/50. dim_user_membership shows a median of 9 activated, non-service users per paying account, and weekly successful interactive requests per user carries an intra-class correlation of 0.25 within an account. The reported lift is 4% at p = 0.05. Separately, the same team wants to evaluate a change to the admission-control queue, which is shared by every account in a region. Give the correct randomisation unit for each test, the effective sample size and corrected p-value for the first, and the design for the second.
Approach
- Separate the two failures of user-level randomisation, because they need different fixes and only one of them is a variance problem. Interference: a treated colleague changes a control colleague's behaviour inside a shared workspace, which biases the estimate toward zero. Correlated outcomes: users inside an account are not independent draws, which understates the variance. Clustering the standard errors fixes the second and leaves the first entirely intact.
- Compute the design effect: 1 + (m - 1) * rho = 1 + 8 * 0.25 = 3.0. Effective sample is 18,000 / 3 = 6,000 users, corresponding to roughly 2,000 accounts. Standard errors are understated by sqrt(3) = 1.73, so a reported t of 1.96 is really 1.13 and the honest two-sided p is about 0.26, not 0.05.
- Re-run the first test randomised on account_id with inference clustered on account_id, or equivalently as a regression on account-level means. The account-means version is the conservative one and is usually easier to defend to a non-specialist audience, at the cost of some efficiency when cluster sizes are very unequal.
- For the queue change, account randomisation fails too, and for a different reason: treated and control accounts contend for the same finite capacity, so the control arm is mechanically affected by the treated arm and the contrast estimates a property of a mixed system rather than the effect of the policy. Randomise time instead, with a switchback alternating the admission policy at the region-by-30-minute-slot level.
- Specify the switchback so it is actually valid. Impose a washout at the start of each slot at least as long as the p99 queue drain time and discard requests enqueued before the switch; randomise slot order rather than strictly alternating, because a fixed alternation aliases with the hourly and weekday traffic cycle; block on hour-of-day so both policies see peak and trough; and cluster inference on the slot, which is the unit that was randomised.
- State the power consequence plainly: the cluster count is slots, not requests. Fourteen days of 30-minute slots gives 672 slots, and the variance to plan against is between-slot variance in the outcome, which is far larger than between-request variance and is what makes switchbacks expensive.
Follow-up
- The intra-class correlation was estimated at 0.25 from a 300-account pilot. If it is really 0.40 the design effect becomes 4.2. How do you plan under that uncertainty rather than betting on the point estimate?
- Under what conditions is user-level randomisation still the right choice even though users share accounts?
- The queue change is expected to leave a four-hour retry backlog. What does carryover of that length do to a 30-minute switchback, and what would you run instead?
Billable units per account jumped while nothing shipped
Billable units per paying account rose 22 percent month over month with no pricing or packaging change. From fct_api_request (account_id, endpoint, http_status, is_retry, idempotency_key, traffic_class, billable_units, request_at) and fct_usage_daily (account_id, sku_code, usage_date, billable_quantity), determine how much of the rise is delivered value and how much is duplicated work. Deliverable: the decomposed figure, the accounts it concentrates in, and a recommendation on whether to report the 22 percent at all.
Approach
- Check the guardrail before the headline: compute the customer-visible server error rate per account for both months, numerator http_status >= 500 and denominator excluding synthetic_monitor and load_test. Metered volume rising alongside an error rate is the known failure mode in this domain.
- Deduplicate logical work by counting billable_units once per (account_id, idempotency_key) at the first successful request rather than once per row. Rows with a null idempotency_key cannot be deduplicated, so report their share as an explicit uncertainty band instead of assuming they are all unique.
- Split by traffic_class before interpreting anything, because one change to a continuous-integration configuration can multiply request volume overnight without a human deciding anything about the product.
- Test concentration: compute the per-account distribution of the increase and its top-decile share. A rise carried by a few accounts scaling one batch job is a different finding from a broad shift and gets a different recommendation.
- Reconcile against fct_usage_daily for the same accounts and dates, and be ready to explain the expected gap in two sentences: request rows include retries and failures carrying zero billable_units, and the usage table restates after first write.
Follow-up
- A client retrying a request the server already completed produces duplicate billed work. What protocol or product change removes that, and what would you measure to confirm it worked?
- If the duplicated volume was genuinely invoiced, what should finance do, and how does that change what belongs in the metric?
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 ↗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 ↗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 ↗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.
Interviewers here are not checking whether you can describe a project. They want the decision you made, why you made it under the information you had, and what changed afterwards that someone else could measure. A story that ends at 'I built a model' has no ending. Say what the model caused, or what you stopped doing because of it.
Talk about a time when you had to work closely with a data engineering…
Talk about a time when you had to work closely with a data engineering team to resolve a bottleneck in your data pipeline.
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
- What did you decide not to do, and why?
- How did you know the outcome was caused by your change?
Describe your experience with Generative AI frameworks. How would you …
Describe your experience with Generative AI frameworks. How would you implement Retrieval-Augmented Generation (RAG) for a client knowledge base?
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on your reasoning.
- Quantify the outcome, including what you would not claim credit for.
Follow-up
- What did you decide not to do, and why?
- How did you know the outcome was caused by your change?
Report an underpowered consumption test to a non-technical executive
An account-randomised packaging change ran six weeks across 900 paying accounts. The effect on billable units per account per month is plus 4.1 percent, with a 95 percent interval from minus 3.2 to plus 11.8 after clustering standard errors at the account and applying the pre-registered winsorisation at the 99th percentile. An executive with no statistical background wants one number this week to decide a full rollout. Produce a three-sentence spoken answer, one chart, and an explicit recommendation of ship, stop or keep running, with the cost of each option stated.
Approach
- The interviewer is probing whether you can be decision-useful without either hiding the uncertainty or hiding behind it. Start from the decision rather than the statistics: establish what the executive would do differently at plus 4 percent versus zero, because if the action is identical the interval does not matter.
- Translate the interval into consequences in units the executive already reasons about. Multiply both endpoints by the cohort's baseline consumption and contracted rates to give an annualised revenue range, so the answer is a range of dollars rather than a range of percentages.
- Price the option to wait. Using the observed variance, state roughly how many additional account-weeks halve the interval width, so keep running becomes a quantified choice instead of a stall.
- Offer a cheaper path to the same decision: a lower-variance proximate outcome such as successful billable units on the new SKU, or CUPED using each account's pre-period consumption, quoting the expected variance reduction as one minus the squared pre-post correlation.
- Give a recommendation and name the single observation that would reverse it. A strong answer commits; a generic one recites the interval and leaves the decision on the table.
Follow-up
- The executive says it clearly works and is just not provable, so ship it. What is your answer?
- How much of the interval width comes from clustering and how much from the revenue tail, and what would you do about each?
- If you had to ship this week with no more data, which guardrail would you watch for the first fortnight and at what threshold would you roll back?
- 01
Talk about a time when you had to work closely with a data engineering team to resolve a bottleneck in your data pipeline.
- 02
Describe your experience with Generative AI frameworks. How would you implement Retrieval-Augmented Generation (RAG) for a client knowledge base?
- 03
An account-randomised packaging change ran six weeks across 900 paying accounts. The effect on billable units per account per month is plus 4.1 percent, with a 95 percent interval from minus 3.2 to plus 11.8 after clustering standard errors at the account and applying the pre-registered winsorisation at the 99th percentile. An executive with no statistical background wants one number this week to decide a full rollout. Produce a three-sentence spoken answer, one chart, and an explicit recommendation of ship, stop or keep running, with the cost of each option stated.
Is this an official Infosys interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Infosys. Rounds and questions reflect what candidates have reported, not a process Infosys has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the coding rounds at Infosys for Data Scientist roles?
The coding evaluations are moderately challenging but highly practical. They do not focus on extreme algorithmic puzzles (such as LeetCode Hard). Instead, they test your ability to write clean, error-free Python code for data manipulation, preprocessing, and writing optimized SQL queries using window functions and CTEs.
PracHub interview research ↗What is the significance of the "Client Round" in the interview process?
The client round is a defining feature of the Infosys process. Because Infosys is a consulting firm, interviewers use this round to evaluate how well you communicate technical concepts, handle feedback, and design solutions tailored to specific business constraints.
PracHub interview research ↗How much focus is there on Generative AI during the technical interviews?
There is a significant and growing focus on Generative AI and LLMs, as these are primary drivers of value for current clients. You should be prepared to discuss how to implement, optimize, and deploy Gen AI solutions, such as RAG pipelines, alongside traditional machine learning models.
PracHub interview research ↗What is the typical timeline from the first interview to receiving an offer?
The entire process typically takes between 3 to 6 weeks, depending on the urgency of the client requirements and candidate availability. The steps are well-structured, and you will receive feedback after each round from the recruitment team.
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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