A Data Scientist at TATA ELXSI plays a pivotal role in harnessing the power of data to drive decision-making and innovation. This position is integral to the company’s mission of delivering cutting-edge solutions across various industries, including automotive, healthcare, and embedded systems. By analyzing complex datasets, you will contribute to developing intelligent systems and products that enhance user experiences and optimize operational efficiencies.
The impact of this role extends beyond mere data analysis; you will be at the forefront of strategic initiatives, driving insights that influence product design, improve customer satisfaction, and create competitive advantages for the business. Working closely with cross-functional teams, you will address complex challenges, leveraging your expertise to shape the future of technology solutions. Expect to work on exciting projects that push the boundaries of what is possible, making your contributions not only valuable but also rewarding.
Application Review
reportedA screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.
What to demonstrate
- Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
- Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
- Whether timeline, location and compensation expectations make the rest of the loop worth scheduling
How to prepare
- Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
- Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
- Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice
Technical Interview
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
Problem-Solving Interview
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.
Behavioral Interview
reportedMost of the weight in this round sits on the disagreement questions. Data work routinely produces an answer someone senior did not want, and the interviewer is trying to learn what you do in that hour. Both failure modes are common: folding as soon as a director pushes back, and treating the pushback as ignorance to be corrected with a better chart. A strong answer usually contains a specific thing the other person knew that you did not, and describes how you found out whether it changed the conclusion.
What to demonstrate
- Whether you can state the other side's argument accurately before you explain why you disagreed
- What you treated as evidence during the disagreement, such as a rerun under their assumption or a holdout check, rather than persuasion technique
- Whether you distinguish being overruled from being wrong, and can give an example of each
How to prepare
- Write out one disagreement where you turned out to be wrong, and say what in the data misled you. Candidates prepare the story where they were right, and the follow-up asks for the other one.
- For your main disagreement story, be ready to say what result would have made you drop your position. If no such result exists, you were not arguing from the data.
- Practise stating the opposing position out loud in one sentence the stakeholder would accept, then continue the story.
Final Interview
reportedWhere a loop ends with a senior leader, that conversation is rarely another skills test. The technical signal already exists by then, so the questions tend to open up: what you would look at first, where a metric you have heard about could mislead, what you would push back on. The decision being made is scope, which in practice means level and how much you would be trusted to own unsupervised. Treating it as a formality is the usual mistake. An open question late in the day is still being scored, and a vague answer reads as someone who has not run anything themselves.
What to demonstrate
- Whether your view of the business has anything specific behind it, given that you are working only from what is public and are expected to say so
- Whether the scope of work you describe owning matches the scope of the role, instead of sitting a level below it
- Whether you can disagree with something concrete and stay useful about it, rather than agreeing with everything said in the room
- Whether your questions are ones only this person could answer, as opposed to ones the recruiter already covered
How to prepare
- Build one view you could defend for two minutes using only public information: what the funnel probably looks like, which metric likely drives decisions, and where that metric could mislead. Being wrong for a stated reason survives this round; having no view does not
- Write down the largest piece of work you have owned from question to decision, who else touched it, and what you decided alone, then check that it reads at the level you are interviewing for
- Prepare one thing you would want changed if you joined and phrase it as a question rather than a verdict, so it opens a conversation instead of closing one
Offer 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.
Using written premium as the denominator of a loss ratio
Premium is written at inception and earned pro rata across the exposure period, so in a growing book written premium runs ahead of earned premium and the loss ratio comes out too low, with the error reversing when the book shrinks. The numerator has the mirror-image problem if it omits incurred-but-not-reported reserves, since recent accident periods then look profitable twice over. Both sides must refer to the same exposure period, which is what an accident-period view at a fixed development age enforces.
Assuming a model is fair because protected attributes are not among its inputs
Postcode, device, tenure, income proxies and even transaction patterns correlate with protected characteristics, so a model can produce a disparate outcome without ever reading the attribute. Credit decisions additionally carry an explainability obligation in many jurisdictions, since a denial has to be accompanied by its principal reasons, which constrains model form and feature engineering rather than being a reporting afterthought. Treating fairness testing and reason-code generation as design constraints from the first model version is far cheaper than retrofitting them to a deployed one.
Ignoring interference between units in a marketplace experiment
Ask whether one unit's treatment can change another unit's outcome through shared inventory, a matching pool, a social graph or a common budget. Where it can, randomise at a level that contains the spillover, such as region or time slice, and say explicitly what that costs you in statistical power.
Answering a product-sense question with a list of features
Answer with a decision and the measurement that would settle it: the hypothesis, the primary metric, the guardrails, and the result that would make you not ship. A feature brainstorm cannot be wrong, which is exactly why it earns no points.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Describe a machine learning project you have worked on and the impact …
Describe a machine learning project you have worked on and the impact it had.
Approach
- 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.
- 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?
Can you explain the concept of overfitting and how to prevent it?
Can you explain the concept of overfitting and how to prevent it?
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Set a baseline first, so any model has something honest to beat.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
Measure calibration of a twelve-month default probability from scratch
fct_loan_application gives application_id, model_pd_12m, model_version, decision, funded_at and loan_id. fct_loan_performance_monthly gives loan_id, months_on_book, days_past_due and charge_off_flag. Define the outcome as ever 90 or more days past due, or charged off, by months_on_book = 12. Without sklearn or scipy, build an equal-count binned reliability table, the expected calibration error, the Brier score and its reliability, resolution and uncertainty components, and report the residual the binned identity leaves behind. Restrict to cohorts that have actually reached 12 months on book.
Approach
- Build the label first and name the population it covers out loud: only funded loans have outcomes, so this measures calibration on the approved population. The declined region is unmeasured, and no binning scheme repairs that.
- Restrict to applications whose loans have reached months_on_book = 12. A cohort observed at 8 months has a mechanically lower default rate and will read as systematic over-prediction that is really just immaturity.
- Bin by equal count, deciles of model_pd_12m through a rank-based cut, not equal width. The PD distribution is heavily right-skewed, so equal-width bins put most of the mass in the first bin and leave the risky bins with single-digit counts whose observed rates mean nothing.
- Per bin compute n, mean predicted, observed rate, and the binomial standard error sqrt(o(1-o)/n) so a gap can be read against noise. ECE is the count-weighted mean absolute gap between mean predicted and observed.
- Compute Brier directly as the mean squared error, then reliability = sum of n_k (pbar_k - obar_k)^2 over N, resolution = sum of n_k (obar_k - obar)^2 over N, uncertainty = obar(1 - obar). Report residual = Brier - (reliability - resolution + uncertainty). That identity is exact only for discrete forecasts, so with binned continuous scores the residual is the within-bin spread of the score; a large one means the bins are too wide to support the decomposition.
- Split by model_version. A mixed-version population can look well calibrated in aggregate while each version is biased in opposite directions.
Worked solution 45 min
- Reduce fct_loan_performance_monthly to one row per loan_id with the maximum days_past_due and any charge_off_flag over months_on_book 0 to 12, plus the maximum months_on_book observed, and keep only loans reaching 12.
- Inner-join to approved and funded applications, and record how many approved applications were dropped for immaturity and how many decisions were declines that never enter the measurement at all.
- Assign deciles with a rank-based cut on model_pd_12m, then aggregate n, mean predicted, observed rate and standard error per bin.
- Compute ECE, Brier, reliability, resolution, uncertainty and the residual, and print all six.
- Repeat the whole computation split by model_version and compare the per-version reliability against the pooled figure.
Follow-up
- AUC is unchanged after a population shift but the reliability curve has moved. What happened, and what do you do about it?
- How would you recalibrate without retraining, and what would you check afterwards?
- The top decile shows observed default well above predicted. Is that a calibration problem or a policy problem?
What libraries are you most comfortable using for data analysis in Pyt…
What libraries are you most comfortable using for data analysis in Python?
Approach
- Say which table is the grain you start from, and join outward from it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
Customers with no credit application, avoiding the NOT IN trap
Count current customers who have never submitted a credit application, broken out by segment. dim_customer is a slowly changing dimension type 2, so restrict to is_current = true, kyc_status = 'verified' and closed_at null. In fct_loan_application, customer_id is null for applicants who were not customers when they applied. Write the anti-join, return segment and customer_count, and state in one line what NOT IN (SELECT customer_id FROM fct_loan_application) returns against this table and why.
Approach
- Pin the dimension to one row per customer first: is_current = true already guarantees that, but say so out loud, because forgetting it multiplies every count by the number of attribute versions a customer has accumulated.
- Write the anti-join as NOT EXISTS with a correlated predicate on customer_id, which evaluates per row and is unaffected by nulls anywhere in the applications table.
- Name the failure explicitly: NOT IN against a nullable column compares each candidate to a set containing NULL, the comparison yields UNKNOWN rather than TRUE, and the whole predicate is therefore never satisfied, so the query returns zero rows.
- If NOT IN is required for some reason, add WHERE customer_id IS NOT NULL inside the subquery, which restores the intended semantics, and note that a LEFT JOIN with an IS NULL filter is equally safe.
- Group by segment and sanity-check the total against the unfiltered current-customer count minus the count of distinct applying customers.
Worked solution 20 min
- SELECT segment, COUNT(*) FROM dim_customer c WHERE c.is_current AND c.kyc_status = 'verified' AND c.closed_at IS NULL.
- Add AND NOT EXISTS (SELECT 1 FROM fct_loan_application a WHERE a.customer_id = c.customer_id).
- Group by segment and order by the count descending.
- Run the NOT IN variant alongside it and record that it returns zero rows, then run it again with IS NOT NULL added to the subquery and confirm the counts match the NOT EXISTS version.
Follow-up
- Rewrite it as a LEFT JOIN with IS NULL and say when you would prefer that form to NOT EXISTS.
- How does the answer change if you want customers who never applied as of a historical date rather than today?
- The applications table has 40,000 rows with a null customer_id. What are those rows, and are they a data quality problem or a product fact?
How would you optimize the performance of your code?
How would you optimize the performance of your code?
Approach
- Fix the population and the time window before naming any metric.
- State what result would change your recommendation, so the answer is falsifiable.
- Decompose the metric into the rates that drive it, and say which one you would check first.
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?
If you had to optimize a recommendation system, what steps would you t…
If you had to optimize a recommendation system, what steps would you take?
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.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
How do you prioritize tasks when working on multiple projects?
How do you prioritize tasks when working on multiple projects?
Approach
- Fix the population and the time window before naming any metric.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- 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?
You are given a dataset with several variables; how would you determin…
You are given a dataset with several variables; how would you determine which are significant predictors?
Approach
- Fix the population and the time window before naming any metric.
- State what result would change your recommendation, so the answer is falsifiable.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
Describe how you would design an A/B test to evaluate a new feature.
Describe how you would design an A/B test to evaluate a new feature.
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- 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
- 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?
What techniques do you use for feature selection?
What techniques do you use for feature selection?
Approach
- Work from the decision backwards to the evidence you would need.
- Clarify what is being asked and what a complete answer would contain.
- Say what you would check first and why it is the highest-information step.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Success metrics for loosening a fraud decline threshold
A risk team proposes lowering the risk_score cutoff that produces auth_result = 'declined_risk_rule'. Settled volume per active customer is the north star; net fraud loss in basis points of settled volume is the guardrail. The two move in opposite directions by construction. Specify the readout: primary metric, guardrail, the maturity window each is read at, and the decision rule agreed before launch. Show the expected-cost arithmetic that sets the cutoff using an average ticket of 200 units, a 1.5 percent contribution margin, 35 percent recovery on fraud losses, and 12 units of downstream value lost per false decline.
Approach
- Refuse the two-metric framing and convert both sides into one currency. Approving a fraudulent transaction costs the amount net of recovery; declining a good one costs the forgone margin plus the downstream value of the customer's reaction. Decline when p times C_FN exceeds (1 minus p) times C_FP, so the break-even probability is p* = C_FP / (C_FP + C_FN).
- Put the numbers in. C_FN = 200 times (1 minus 0.35) = 130, C_FP = 200 times 0.015 plus 12 = 15, so p* = 15 / 145 = 10.3 percent. Then show the threshold is amount-dependent: at a 2,000 ticket C_FN = 1,300 and C_FP = 42, giving p* = 3.1 percent, so a single global cutoff is already the wrong shape before any tuning starts.
- State the precondition that makes this arithmetic legal: risk_score has to be calibrated, so that a score of 0.10 corresponds to an observed 10 percent fraud rate. A score that only ranks makes p* meaningless. Check the reliability curve before quoting any cutoff to anyone.
- Set the maturity windows separately. Volume is readable within days, fraud loss is not, so the guardrail is read only on transaction months with at least 120 days of dispute maturity and the decision stays open until then, or a leading indicator is agreed in advance with its bias written down.
- Agree the stopping rule before launch in the right units: revert if matured net fraud loss per unit of incremental settled volume exceeds the figure implied by p*. Fraud loss is supposed to rise when the cutoff loosens, so a rule that triggers on any rise is a rule that was never going to allow the change.
- Report the swap set rather than portfolio totals: the transactions the new cutoff approves that the old one declined, and their realised loss rate. Portfolio aggregates dilute the change into invisibility.
Worked solution 30 min
- Compute p* at ticket sizes of 50, 200 and 2,000 with the given margin, recovery and false-decline cost, and tabulate them.
- Bucket historical declined_risk_rule authorizations by risk_score decile and, for each bucket, write down what outcome data exists and what does not.
- Write the readout spec: primary metric, guardrail, the 120-day maturity rule, the swap-set table and the numeric stopping rule.
- Write the calibration precondition in two sentences and say how you would test it.
Follow-up
- Fraud loss in basis points falls after launch. Name two ways that happens without any improvement in decisioning.
- How do you keep observing outcomes in the region the rule still declines?
- What changes if the 12 units of downstream value is a guess with no evidence behind it?
Approval rate rose in every band yet fell overall
Monthly application approval rate on fct_loan_application fell from 62 to 57 percent. Cut by bureau_score band, the rate rose in every band, including the null-bureau band. Columns: application_id, channel, submitted_at, requested_amount_minor, declared_annual_income_minor, bureau_score, model_pd_12m, model_version, policy_rule_hits, decision, decided_by, decision_at. The denominator is decision in ('approve','decline'). Explain the arithmetic, quantify how much of the five-point fall is mix versus within-band movement, and say what you would tell the team that owns acquisition.
Approach
- Confirm the paradox is real rather than a banding artefact. Rebuild the bands on fixed cutpoints taken from the earlier period, because quantile bands re-cut each month move with the population and can manufacture this pattern on their own.
- Compute the exact decomposition rather than describing it: within = sum of w_i0 * (r_i1 - r_i0), mix = sum of r_i0 * (w_i1 - w_i0), interaction = sum of (w_i1 - w_i0) * (r_i1 - r_i0). The three terms sum identically to the change in the blended rate, so the report can state the split.
- Attribute the weight change by cutting the same fixed bands by channel. Keep null bureau_score as its own band; a thin file is a population signal, not missing data to be imputed away.
- Check whether the new arrivals also changed the population inside a band, by comparing requested_amount_minor and declared_annual_income_minor distributions within one band across the two months.
- Deliver two numbers rather than one story: policy is looser in every band, and the funnel is being fed a different population. Those have different owners and different fixes.
Follow-up
- If the new channel is profitable at its own approval rate, is the blended fall a problem at all?
- How would you present this so that nobody reads the blended series unaccompanied again?
- What breaks if you fix the mix by reweighting to a frozen band distribution every month?
Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Breadth pass: query fluency
- Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
- For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
- Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.
Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Breadth pass: statistics and inference
- Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
- Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
- Rewrite the two weakest answers the following morning from memory in full sentences.
Deliverable: Ten graded answers with an honest count of exact hits.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Breadth pass: modelling
- Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
- Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
- Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.
Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.
Practice prompt ↗Practice prompt ↗04Breadth pass: product judgement
- Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
- For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
- Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.
Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Depth, first area
- Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
- Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
- Re-solve the two you failed the same evening with notes closed.
Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.
Practice prompt ↗Practice prompt ↗06Depth, second area, and the seam between them
- Repeat the depth protocol on the second-ranked area with the same six-problem structure.
- Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
- Solve your own combined problem end to end and note where the handoff between the two areas cost you time.
Deliverable: One combined problem, solved end to end, with the handoff failure written down.
Practice prompt ↗Practice prompt ↗07Integration and re-measurement
- Re-run the six prompts from day one under the same clock and compare both correctness and time.
- Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
- Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.
Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.
Describe your experience working in a cross-functional team. What role…
Describe your experience working in a cross-functional team. What role did you play?
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.
- State the situation in two sentences and spend the rest on your reasoning.
Follow-up
- What would you do differently if you ran that project again?
- How did you know the outcome was caused by your change?
Turn a one-line fraud-number request into a scoped brief
A stakeholder messages: what is our fraud rate, and is it going up? You have fct_payment_authorization, fct_card_dispute and dim_customer. At least four defensible answers exist: count-weighted or value-weighted, attributed to the transaction month or to the dispute filing month, and gross or net of recoveries and successful representments. You get one reply before someone else produces an uncaveated number. Write that reply: the clarifying questions you ask, the single default you will produce if nobody answers, and what the default excludes.
Approach
- Establish the decision behind the question first, because a risk-rule change, a board number and a merchant contract negotiation need different denominators, and asking which one is not stalling.
- Offer a short menu rather than an open question: a stakeholder can choose between two named options but cannot specify a denominator from scratch.
- Commit to a default so the reply is useful even if nobody answers, for example net fraud loss in basis points of settled volume, attributed to the requested_at month, matured months only.
- State the exclusions in the same breath as the default: non-fraud dispute categories, transaction months with less than 120 days of maturity, and first-party abuse that arrives coded as consumer_dispute.
- Give a delivery time for the default and a longer one for the fuller cut, so the choice between them carries a visible cost.
Follow-up
- They come back wanting it by merchant for a contract negotiation. What changes in the definition and in the maturity rule?
- How would you separate first-party abuse from third-party fraud in this data, and what would you refuse to conclude from the split?
Recommend a decision whose true outcome matures a year later
An underwriting rule change must be decided in six weeks. Its real outcome, the vintage 90-plus rate at months_on_book 12 in fct_loan_performance_monthly, matures in a year. The executive wants a yes or no, not a range. Randomising the credit decision across the whole population is not available. Name the leading indicator you would accept, state its bias and the direction of that bias, define the decision rule and stopping condition before any rollout starts, and say what reading would make you recommend reversing the change.
Approach
- Fix the readout before the rollout, because a readout chosen after the data arrives is a story rather than a decision rule: indicator, window, threshold and reversal condition all go in writing first.
- Choose the leading indicator on its measured relationship to the matured outcome in historical vintages rather than on availability. Early delinquency, typically the share reaching dpd_1_29 or missing a first scheduled payment by months_on_book 3, is the usual candidate, and you quantify how well it predicted the 12-month rate across past cohorts.
- State the bias and its direction plainly: early delinquency under-represents default that emerges later and is contaminated by servicing and payment-date effects, so treat it as a floor on risk rather than an estimate of it.
- Buy identification where full randomisation is unavailable: a narrow randomised approval band around the cutoff, or a staged rollout by channel or region read as a difference-in-differences, with the parallel-trends assumption stated and checked in the pre-period rather than assumed.
- Give the executive the binary they asked for with the trigger attached in the same sentence: yes, conditional on the month-3 indicator staying inside a stated band, with an automatic hold if it breaches.
Follow-up
- How would you validate that the month-3 indicator predicts the 12-month outcome, and what evidence would invalidate it mid-rollout?
- Compliance refuses a randomised band. What is your next-best identification strategy, and what precision do you lose by taking it?
- 01
Describe your experience working in a cross-functional team. What role did you play?
- 02
A stakeholder messages: what is our fraud rate, and is it going up? You have fct_payment_authorization, fct_card_dispute and dim_customer. At least four defensible answers exist: count-weighted or value-weighted, attributed to the transaction month or to the dispute filing month, and gross or net of recoveries and successful representments. You get one reply before someone else produces an uncaveated number. Write that reply: the clarifying questions you ask, the single default you will produce if nobody answers, and what the default excludes.
- 03
An underwriting rule change must be decided in six weeks. Its real outcome, the vintage 90-plus rate at months_on_book 12 in fct_loan_performance_monthly, matures in a year. The executive wants a yes or no, not a range. Randomising the credit decision across the whole population is not available. Name the leading indicator you would accept, state its bias and the direction of that bias, define the decision rule and stopping condition before any rollout starts, and say what reading would make you recommend reversing the change.
Is this an official TATA ELXSI interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at TATA ELXSI. Rounds and questions reflect what candidates have reported, not a process TATA ELXSI has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the interview process?
The interview process at TATA ELXSI is generally regarded as rigorous, with a good mix of technical and behavioral assessments. Candidates should prepare thoroughly, dedicating several weeks to study and practice.
PracHub interview research ↗What distinguishes successful candidates?
Successful candidates typically demonstrate a strong grasp of data science principles, the ability to communicate their findings, and a collaborative mindset. They also align well with the company's values of innovation and customer focus.
PracHub interview research ↗What is the culture like at TATA ELXSI?
The culture at TATA ELXSI is collaborative and driven by a commitment to excellence. The company encourages innovation and values team members who contribute to a positive work environment.
PracHub interview research ↗What is the typical timeline from initial screen to offer?
Candidates can expect a timeline of about 4 to 6 weeks from initial screening to receiving an offer, depending on the availability of interviewers and the number of candidates.
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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