A Data Scientist at Idfc First Bank plays a pivotal role in driving the bank's digital-first philosophy. Operating at the intersection of finance and technology, you will build data-driven solutions that directly impact millions of customers. The bank relies heavily on data science to optimize credit underwriting, enhance fraud detection, personalize customer experiences, and pioneer next-generation technologies like Generative AI.
In this role, you are not just a model builder; you are a strategic partner to the business. You will work on massive datasets to extract actionable insights, design robust machine learning pipelines, and deploy models that operate in real-time. Whether you are placed in the core Risk Analytics team, the Customer Analytics division, or the specialized Gen AI team, your work will directly influence the bank's efficiency, risk management, and profitability.
Idfc First Bank values clean, scalable code and rigorous mathematical foundations. Candidates who succeed here can translate complex financial and behavioral data into highly accurate predictive models while keeping ethical AI and regulatory compliance at the forefront of their designs.
Preparation focus
editorialNo round sequence has been reported for this company, so work the categories below and confirm the format with your recruiter.
What to demonstrate
- Breadth across SQL, experimentation and product reasoning
- Ability to state assumptions before choosing a method
How to prepare
- Drill the practice exercises below and time yourself
- Prepare three quantified stories about decisions you drove
PracHub editorial advice for the preparation topics above.
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.
Counting authorizations instead of weighting them, and summing amounts across currencies
Declines skew toward high-value, cross-border and card-not-present transactions, so an unweighted approval rate can sit flat while approved value falls. Merchant retry logic also turns one declined purchase into several rows, inflating the denominator by an amount that varies by merchant and by decline reason. Amounts are held in the minor unit of the transaction currency and that unit is not always two decimals, since some currencies have none and some have three, so summing amount_minor across currencies produces a figure with no interpretation at all.
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.
Reading experiment results before checking the arm split
Compare observed arm counts against the intended allocation ratio, not an assumed even split, and set the alarm far below the conventional 0.05: at 0.05 roughly one healthy experiment in twenty trips it, which is why sample-ratio checks usually run at p < 0.001 or stricter. The test's power scales with sample size, so it misses a real diversion on a small experiment and fires on an imbalance too small to move the estimate on a very large one. A flag means go find the assignment or logging fault before reading any outcome, not report a mismatch.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the concept of hypothesis testing and how you set up null and …
Explain the concept of hypothesis testing and how you set up null and alternative hypotheses.
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Translate the result into the decision it informs, in one plain sentence.
- Sanity-check the answer against a simple bound or a simulated case.
Follow-up
- What sample size would you need to detect an effect half this size?
- Which assumption here is most likely to be violated in practice?
What is the difference between a normal distribution and a t-distribut…
What is the difference between a normal distribution and a t-distribution, and when would you use the latter?
Approach
- Say what the estimate is of, and over what population it generalises.
- Sanity-check the answer against a simple bound or a simulated case.
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
Follow-up
- What sample size would you need to detect an effect half this size?
- How would you explain this result to someone who does not know statistics?
How do you address multicollinearity in econometrics and regression mo…
How do you address multicollinearity in econometrics and regression models?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Set a baseline first, so any model has something honest to beat.
- Pick an evaluation metric that matches the cost of each error type, not a default.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
Implement accident-quarter loss ratio at twelve months development
fct_policy_period_monthly arrives as a stack of month-end snapshots: each row carries valuation_month alongside as_of_month, policy_id, product_line, written_premium_minor, earned_premium_minor, paid_loss_minor, case_reserve_minor, ibnr_reserve_minor and loss_adjustment_expense_minor. Compute the accident-quarter loss ratio at exactly 12 months of development: incurred losses over earned premium, both taken from rows whose as_of_month falls in the accident quarter, read from the snapshot 12 months after that quarter closes. Report quarters that cannot reach that age as incomplete rather than dropping them.
Approach
- Derive accident_quarter from as_of_month, then define the evaluation snapshot per quarter as valuation_month equal to the quarter's final month plus twelve months. Every figure in the ratio comes from that one snapshot, not from whichever snapshot happens to be newest.
- Numerator is paid_loss_minor plus case_reserve_minor plus ibnr_reserve_minor over the accident quarter's rows in that snapshot. Loss adjustment expense may be included or not, but the choice applies to every quarter and is named in an output column.
- Denominator is earned_premium_minor over the same rows. Written premium is booked in full at inception, so in a growing book it runs ahead of earned premium and drags the ratio down, with the error reversing when the book shrinks.
- Left-join the full quarter list against available valuation months so a quarter with no 12-month snapshot yields status incomplete and a null ratio, instead of disappearing and shortening the series without saying so.
- Split by product_line, since both the loss ratio level and the speed of development differ by line, and a blended series moves with mix as much as with experience.
Worked solution 30 min
- Add accident_quarter and a target_valuation column equal to the quarter end plus twelve months.
- Filter rows to those where valuation_month equals the row's target_valuation, then assert each accident_quarter has exactly one distinct valuation_month left.
- Aggregate incurred and earned premium by accident_quarter and product_line and take the ratio.
- Reindex against the full list of accident quarters and product lines, marking rows with no matching snapshot as incomplete with a null ratio.
- Recompute one quarter by hand on a five-policy subset and confirm it matches.
Follow-up
- The most recent complete quarter came in four points better than the one before. What do you check before calling it an improvement?
- How would you estimate the 12-month figure for a quarter that is only 6 months developed, and how would you label the estimate?
- Why can an expense ratio legitimately use a different denominator from the loss ratio in the same presentation?
Vintage ninety-plus rate at twelve months on book
From fct_loan_performance_monthly, build a vintage table keyed on origination_month: the share of each cohort that ever reached days_past_due of 90 or more, or charge_off_flag = true, at or before months_on_book = 12. Restructuring resets days_past_due, so for any loan with restructured_flag true, evaluate only the month ends strictly before its first restructured month. Return origination_month, loans_funded, bad_loans and bad_rate. Exclude any cohort that does not yet have a months_on_book = 12 observation for every loan still on book.
Approach
- Establish the cohort denominator from the first month end each loan appears at, months_on_book = 0, so a loan is counted once in its origination_month rather than once per monthly row.
- Find each loan's first restructured month with MIN(as_of_month_end) FILTER (WHERE restructured_flag) OVER (PARTITION BY loan_id), or the equivalent grouped subquery, and keep it null for loans never restructured.
- Flag a loan bad if any row with months_on_book <= 12 and as_of_month_end earlier than that first restructured month has days_past_due >= 90 or charge_off_flag = true, which is what 'pre-restructure worst state' means in practice.
- Gate maturity by requiring the cohort's newest month end to be at least 12 months after origination_month, and report immature cohorts as incomplete rather than letting them appear at a flattering low rate.
- Aggregate to one row per origination_month and read the column downward, not across calendar time, because the whole point is comparing cohorts at equal age.
Worked solution 35 min
- CTE 1: per loan_id, derive origination_month, the first restructured month end, and the maximum months_on_book observed.
- CTE 2: join back to the monthly rows, filter to months_on_book <= 12 and to month ends before any restructure, then flag bad with a BOOL_OR.
- CTE 3: aggregate to origination_month with COUNT() as loans_funded and COUNT() FILTER (WHERE is_bad) as bad_loans.
- Apply the maturity gate and compute bad_rate with a numeric cast.
Follow-up
- Should a restructure inside 12 months count as bad in its own right? Argue both sides and say what you would actually ship.
- A loan that prepaid in full at month 4 never had a chance to go 90 days past due. In or out of the denominator, and why?
- The 2025-11 cohort is two points worse at month 12 than its neighbours. What three queries do you run before you call it a credit-quality change?
Attribute chargebacks to transaction month without fanning out
Report the matured first-chargeback rate. Numerator: fct_card_dispute rows whose dispute_stage is first_chargeback, representment, pre_arbitration or arbitration, attributed to the requested_at month of the linked fct_payment_authorization row rather than to opened_at. Denominator: settled authorizations in that same transaction month, meaning settled_at is not null and is_reversal is false. One authorization can carry more than one dispute case. Report only transaction months whose last day is at least 120 days old, and mark the remainder incomplete rather than showing them as low.
Approach
- Compute the denominator from fct_payment_authorization alone, grouped by the requested_at month, before any dispute table is mentioned, so the join cannot touch it.
- Compute the numerator as a separate aggregate: join disputes to their authorization only to recover requested_at, then count dispute rows per transaction month with the stage filter.
- Join the two aggregates on the month key, which keeps the grain at one row per month and makes double counting structurally impossible rather than something you have to remember.
- Apply the maturity gate on the month's last day plus 120 days against the current date, because a transaction on the final day of the month is the least mature one in the cohort.
- Emit an explicit status column of 'final' or 'incomplete' instead of filtering immature months away silently, so a reader cannot mistake absence for zero.
Follow-up
- Should the numerator count dispute cases or disputed authorizations? Which one reconciles to the loss line, and which one to the operations queue?
- Some reason codes allow filing well beyond 120 days. How would you estimate the tail on a month you have decided to call final?
- How would you present the incomplete months so that a weekly reader does not read the right-hand slope as an improvement?
Estimate the marginal effect of approval at a score cutoff
Policy auto-approves applications with bureau_score at or above 660 and routes 640 to 659 to manual review, which approves about 35 percent of them. You cannot randomise approvals. Using fct_loan_application and a bureau-sourced panel that reports 12-month serious delinquency on any trade line for approved and declined applicants alike, estimate the effect of approval at the margin. State the identifying assumptions you would test rather than assert, the estimator and its tuning choices, and exactly what the estimate does and does not license.
Approach
- Set the design up as a fuzzy regression discontinuity, because crossing 660 changes approval probability sharply but not from zero to one. Let Z = 1{bureau_score >= 660} be the instrument, approval be the treatment, and 12-month serious delinquency be the outcome. The estimate is the ratio of the jump in the outcome to the jump in approval probability, which is two-stage least squares with Z as the instrument and identifies a local average treatment effect for compliers at the cutoff.
- Insist on the bureau panel as the outcome source. Funded loans exist on both sides of 660 only because manual review approves some applicants below it, and those are selected on whatever the reviewer saw; an outcome defined only on funded loans would reintroduce exactly the selection the design is meant to remove.
- Test the assumptions instead of listing them. Check density continuity of bureau_score at 660 with a McCrary-style or local-polynomial density test, since brokers and applicants can sometimes trigger a bureau refresh and a heaped density at the threshold kills the design. Check that pre-determined covariates, including declared_annual_income_minor, channel, product_code and model_version, are continuous at the cutoff. Check policy_rule_hits and the pricing table for any other rule that fires at exactly 660, because a second discontinuity at the same point is not separable from the first.
- Choose the estimator deliberately. Local linear regression with a triangular kernel and an MSE-optimal bandwidth, with bias-corrected robust confidence intervals. Do not fit a high-order global polynomial, which puts weight on observations far from the cutoff and produces artefacts at the boundary. Because bureau_score is an integer, the running variable has mass points, so cluster standard errors by score value or use an approach designed for discrete running variables.
- Respect outcome maturity. Include only application cohorts with a full 12 months of bureau observation; a recent cohort with partial observation will look cleaner and will drag the estimate.
- State the limits in the deliverable. The estimate is the effect at 660 on applicants whose approval status is determined by the threshold. It does not license moving the cutoff to 620, because both the first stage and the outcome relationship differ away from 660 and because a policy change at scale shifts the applicant mix. Report bandwidth sensitivity alongside the point estimate.
Worked solution 40 min
- Assemble the application-level panel: bureau_score, decision, funded status, and the bureau-sourced 12-month serious delinquency flag, restricted to cohorts with 12 full months of outcome observation.
- Plot the first stage, being approval probability against bureau_score in one-point bins, and confirm a visible jump of roughly 0.65 at 660 (about 1.00 above against about 0.35 below).
- Plot the reduced form, being delinquency against bureau_score in the same bins, and read the jump at 660 off a local linear fit on each side.
- Estimate the Wald ratio as reduced form over first stage, equivalently two-stage least squares with Z = 1{score >= 660}, using a triangular kernel, an MSE-optimal bandwidth, bias-corrected robust intervals, and standard errors clustered by integer score.
- Run the assumption battery: density continuity at 660, covariate continuity, placebo cutoffs at 650 and 670, and bandwidth sensitivity at 0.5x and 2x the selected bandwidth.
- Write the result as a complier average effect at 660 with its interval, its bandwidth sensitivity table, and an explicit sentence on what it does not license.
Follow-up
- Manual reviewers below the cutoff see documents the score does not. What does that do to the monotonicity assumption, and how would you look for defiers?
- You are offered a randomised approval band across 650 to 659 for one quarter. What does it buy you that the discontinuity does not, and how would you price the expected loss of running it?
- The density test shows a modest pile-up just above 660. What are the possible mechanisms, and which of them leave the design usable?
Cut variance on a heavy-tailed settled-volume metric
The primary metric is settled payment volume per customer per month in one reporting currency, randomised by customer_id, computed from fct_payment_authorization rows that are approved, captured and not reversed. The available population powers a minimum detectable effect of 3.0 percent, but the plausible effect is about 1.2 percent. You have eight pre-randomisation weeks of the same metric per customer, and the distribution is heavily right-skewed. Propose a variance-reduction plan, state the estimator and its preconditions, quantify the improvement you expect, and say whether it closes the gap.
Approach
- Apply CUPED with the pre-period metric as the covariate. Define Y_adj = Y - theta * (X - Xbar) with theta = Cov(Y, X) / Var(X) estimated on the pooled experiment data. The variance of the adjusted difference is Var(Y) * (1 - rho^2) where rho is the correlation between Y and X, so the entire benefit is determined by rho and nothing else.
- Check the precondition that makes the estimator unbiased: X must be measured strictly before assignment. A covariate computed over any window that overlaps the test period absorbs part of the treatment effect and biases the estimate toward zero. Pin the covariate window end date to the randomisation timestamp, not to the analysis date.
- Handle customers with no pre-period. Set X to zero plus a no-history indicator, or stratify them as their own cell. Either is valid because the imputation rule is fixed before assignment and does not depend on the arm; what is invalid is imputing with an arm-specific mean.
- Attack the skew separately, because CUPED reduces variance but does not make a heavy tail light. Pre-register a winsorisation point such as the 99.5th percentile of the pre-period distribution, state plainly that this changes the estimand from the mean to a capped mean, and report both. Capping at a pre-period quantile rather than a post-period one keeps the cap from depending on the treatment.
- Stratify the assignment on pre-period decile crossed with segment, which removes the same variance CUPED does when the covariate is the stratifying variable and additionally guarantees balance rather than achieving it in expectation. Post-stratified analysis is the fallback if assignment is already running.
- Pin the currency conversion to a fixed rate table dated at test start. Using settlement_fx_rate as realised injects market movement into the metric, and if the arms have different currency mixes it injects it unequally.
Follow-up
- Your pre-period correlation is 0.62 on existing customers and near zero on customers onboarded in the last month. How do you report a single effect across both groups without the low-correlation group eating the gains?
- Someone proposes using a post-randomisation covariate that correlates 0.9 with the outcome. Why is that not a better CUPED covariate?
- If you winsorise, what do you say to a sponsor who points out that the capped customers are the ones the business cares about most?
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?
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 ↗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.
Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.
How do you handle highly skewed distributions in financial transaction…
How do you handle highly skewed distributions in financial transaction data?
Approach
- Close with what you would do differently, concretely.
- Quantify the outcome, including what you would not claim credit for.
- Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
Explain the physical significance of a p-value and how you would expla…
Explain the physical significance of a p-value and how you would explain it to a non-technical stakeholder.
Approach
- Pick a story where you drove the decision, not one where you observed it.
- Quantify the outcome, including what you would not claim credit for.
- Close with what you would do differently, concretely.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
State honestly what your cutoff change actually contributed
Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.
Approach
- Define the counterfactual before computing anything: the claim is not what happened after the change, it is what would have happened had the old cutoff scored the same applications, which means replaying the old threshold on the post-change population.
- Build the swap set: applications the new cutoff approves that the old one declined, applications the old one approved that the new one declines, and everyone else held out as unaffected. Only the swap groups carry your effect. Note that the swap-out group has no outcome under the new rule, because those loans were never funded, so its forgone margin must be estimated from matched pre-change approvals rather than observed.
- Price each swap group at months_on_book equal to 12 on the measure the cutoff was meant to move: interest and fees collected, minus net charge-offs, minus funding cost at the internal transfer rate.
- Strip the confounders explicitly. Cohorts affected by the bureau attribute change are either recomputed on the old attribute or excluded; channel mix is held fixed by reweighting to the pre-change mix; funding cost is charged at the rate in force each month rather than one blended average.
- State the residual you will not claim, with its size, and give a range rather than a point wherever cohorts have not yet reached 12 months on book.
Follow-up
- The swap-in group is only 6 percent of applications. How does that change the way you present the number, and to whom?
- What would you have needed to set up at launch to make this attribution clean, and why was a randomised band around the cutoff not used?
- 01
How do you handle highly skewed distributions in financial transaction data?
- 02
Explain the physical significance of a p-value and how you would explain it to a non-technical stakeholder.
- 03
Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.
Is this an official Idfc First Bank interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Idfc First Bank. Rounds and questions reflect what candidates have reported, not a process Idfc First Bank 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 the Data Scientist role?
The process is highly technical and balanced. You will face rigorous coding and SQL tests, followed by deep theoretical discussions on statistics and machine learning. Be prepared to explain the mathematical equations behind your models.
PracHub interview research ↗What differentiates successful candidates at Idfc First Bank?
Successful candidates demonstrate a strong sense of ownership and business curiosity. They do not just build models in isolation; they understand how their work impacts the bank's operational metrics, customer experience, and regulatory compliance.
PracHub interview research ↗How much preparation time is recommended?
Given the breadth of topics—ranging from coding and SQL to statistics, puzzles, and machine learning—most candidates benefit from 3 to 4 weeks of focused preparation.
PracHub interview research ↗Is prior banking experience mandatory?
No, prior banking experience is not mandatory, but you must show a strong appetite for learning financial concepts and be able to apply your analytical skills to banking use cases during the case study rounds.
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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