PNC Financial Services Group · Data Scientist
Updated · 2026-09-22

PNC Financial Services Group Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

As a Data Scientist at PNC Financial Services Group, you play a pivotal role in harnessing data to drive business insights and enhance customer experiences. Your work is crucial to developing analytical solutions that inform strategic decisions, optimize operational processes, and create innovative financial products. By leveraging advanced statistical methods, machine learning algorithms, and data visualization techniques, you will contribute to projects that impact millions of customers and the overall efficiency of the organization.

In modelling rounds the live question is usually why this model class for this problem, and how you would know six weeks after launch that it is still working. Deriving gradients by hand is rarely what is being probed.

PNC Financial Services Group candidates report 3 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Set fraud thresholds by expected costReport only matured cohorts for loss metricsDecompose expected loss into PD, LGD, EAD

34 min read

Browse Data Scientist questions

See the practice prompts

16Practice promptsAcross five skill areas
3With worked solutionsIncluded in the practice prompts

As a Data Scientist at PNC Financial Services Group, you play a pivotal role in harnessing data to drive business insights and enhance customer experiences. Your work is crucial to developing analytical solutions that inform strategic decisions, optimize operational processes, and create innovative financial products. By leveraging advanced statistical methods, machine learning algorithms, and data visualization techniques, you will contribute to projects that impact millions of customers and the overall efficiency of the organization.

This position requires a blend of technical expertise and business acumen. You'll collaborate with cross-functional teams, including product managers, software engineers, and business analysts, to identify opportunities for data-driven enhancements. The work environment at PNC is collaborative and inclusive, encouraging creativity and innovation while focusing on delivering tangible results that align with the company's mission of providing excellent financial services.

You can expect to engage with real-world applications of data science, such as risk assessment models, customer segmentation analyses, and predictive analytics for marketing strategies. This role not only offers the chance to work on complex data challenges but also provides a meaningful opportunity to influence the direction of financial services through data-driven insights.

01

Initial Screening

reported

Data Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.

What to demonstrate

  • Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
  • Whether you name what you have not done instead of stretching to cover every line of the posting
  • Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage

How to prepare

  • Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
  • Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
  • Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live
PracHub interview research ↗
02

Technical Assessment

reported

Before anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.

What to demonstrate

  • Whether an ambiguous term becomes a specific column and filter before any computation happens
  • Whether you read the schema for keys and cardinality rather than only for column names
  • Whether the result answers the question at the grain it was asked at, per user or per session or per day

How to prepare

  • Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
  • On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
  • Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over
PracHub interview research ↗
03

Team Discussions

reported

An added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.

What to demonstrate

  • Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
  • Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
  • How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact

How to prepare

  • Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
  • For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
  • Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.
PracHub interview research ↗

PracHub editorial advice for the preparation topics above.

01

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.

02

Averaging delinquency across a book that is growing

A loan three months old cannot be 90 days past due, so a portfolio with many recent originations reports a low blended 90+ rate purely from age mix. The blended rate falls fastest exactly when originations grow fastest, which is precisely when credit quality most needs watching, so the metric moves in the reassuring direction during the riskiest period. Only comparisons at equal months on book are valid, which is what a vintage or roll-rate view enforces.

03

Treating a non-significant result as proof of no effect

Say whether the confidence interval excludes the effect sizes you would have cared about. If it does not, the honest reading is that the test was underpowered, so report the minimum detectable effect the design could have found and what sample size would resolve it.

04

Writing SQL without stating NULL and tie-breaking behaviour

Before calling a query finished, say what it does with NULLs, ties and empty groups. NOT IN against a subquery containing a single NULL returns no rows at all, and RANK, DENSE_RANK and ROW_NUMBER differ precisely on ties, so name which one the question requires.

Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.

13 technical prompts3 include a worked solution

What steps would you take to improve the accuracy of a predictive mode…

medium
machine learning and modelling

What steps would you take to improve the accuracy of a predictive model that is underperforming?

Approach
  1. Frame the prediction: the label, the moment of prediction, and the action it triggers.
  2. Say how the offline result would be validated online before it is trusted.
  3. Check what information would not exist at prediction time, and exclude it.
Follow-up
  • How would you choose the decision threshold, and who owns that choice?
  • Where could label leakage enter this setup?

What is your approach to validating a predictive model?

medium
machine learning and modelling

What is your approach to validating a predictive model?

Approach
  1. Check what information would not exist at prediction time, and exclude it.
  2. Pick an evaluation metric that matches the cost of each error type, not a default.
  3. 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?

Collapse retry chains and compute a dollar-weighted approval rate

mediumWorked solution
sessionisationwindow functionsdollar-weighted rates

fct_payment_authorization gives auth_id, card_token_id, merchant_id, amount_minor, transaction_currency, requested_at, auth_result, is_reversal, channel and issuer_country. Two reference frames give the minor-unit exponent per currency and a daily rate to one reporting currency. Collapse retry chains first: attempts sharing card_token_id, merchant_id and amount_minor whose consecutive gaps are under 15 minutes form a single attempt, whose outcome is its last row. Exclude reversals and zero-amount verifications. Return a 7-day rolling dollar-weighted approval rate by channel and issuer_country.

Approach
  1. Filter before grouping: drop is_reversal rows and zero-amount verifications, since neither is a purchase attempt and both would otherwise sit in the denominator.
  2. Sort by card_token_id, merchant_id, amount_minor and requested_at, take the gap to the previous row within that key, mark a chain start where the gap exceeds 15 minutes or the key changes, and label chains with a cumulative sum of that flag. This is a gap rule between consecutive attempts, not a fixed clock bucket, so a chain may span more than 15 minutes in total.
  3. Keep each chain's terminal row by requested_at. If a retry was approved, the purchase was approved; keeping the first row reports the decline that caused the retry as the outcome.
  4. Convert amounts exactly once: amount_minor divided by 10 to the power of the currency exponent, multiplied by the reference rate for the authorization date. Do not reach for settlement_fx_rate, which is null on precisely the declined rows the denominator needs.
  5. Build the rolling window as a ratio of two rolling sums, approved value over total value, per channel and issuer_country. A rolling mean of daily ratios weights a quiet Sunday the same as a busy Friday.
Worked solution 35 min
  1. Filter out reversals and zero-amount rows, then sort by the chain key and requested_at.
  2. Compute the within-key time difference, derive the chain start flag and the chain id, and take the last row per chain with groupby(chain_id).tail(1) after sorting.
  3. Join the exponent and daily rate tables, compute value_reporting, and assert no nulls remain after the join.
  4. Aggregate approved value and total value to a daily grain by channel and issuer_country, reindex to a complete date range per group so missing days are zero rather than absent.
  5. Take 7-day rolling sums of both columns and divide, then confirm one hand-picked group-day against a direct filter.
EXPECTED RESULTA DataFrame keyed by date, channel and issuer_country with approved_value, total_value and approval_rate. The collapsed attempt count is materially below the raw row count, with the gap concentrated in declined ecommerce rows, and ecommerce sits below card_present.
Follow-up
  • The count-weighted rate is flat while the dollar-weighted rate falls 80 basis points. What do you look at first?
  • How would you choose the 15-minute window rather than inheriting it?
  • A merchant moves from two retries to five. Which of your two rates moves, and is that a real change in approval quality?

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.

Small steps. Visible outcomes.0 / 7 completed
ONE WEEK · YOUR PACE

Prepare, practise & reflect

One practical outcome each day. Spend longer where you need it.

0 / 7 done
01Breadth 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.

Nearly every data role forces a trade between the analysis you want and the one that fits the decision window. Prepare a case where you deliberately shipped something less rigorous, named the weakness to the person relying on it, and said what would change your answer. The naming is the part interviewers listen for.

How do you handle feedback and criticism?

medium
behavioural and stakeholder questions

How do you handle feedback and criticism?

Approach
  1. Quantify the outcome, including what you would not claim credit for.
  2. Pick a story where you drove the decision, not one where you observed it.
  3. Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
  • What did you decide not to do, and why?
  • What would you do differently if you ran that project again?

Explain an incomplete dispute chart to a non-technical executive

easy
dispute maturitystakeholder communicationright-censoring

A finance lead is looking at first-chargeback rate by transaction month, built from fct_card_dispute joined to fct_payment_authorization on auth_id and attributed to requested_at. The last three months slope sharply down and the lead wants to announce a fraud improvement at tomorrow's review. Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, so those months are not complete. In five minutes, with no statistics vocabulary, explain why the decline is not yet evidence and say exactly what you would put on the slide instead.

Approach
  1. Lead with the mechanism in the listener's own terms, not with the statistical name for it: a dispute is attributed to the month the transaction happened, but it can be filed up to roughly 120 days later, so recent months contain only the disputes filed so far.
  2. Show completeness rather than arguing about the rate: for each transaction month, plot the share of its eventual disputes already filed, estimated from months that are fully matured. The last three months will sit visibly below 100 percent.
  3. Replace the chart with two artefacts: a matured series that stops 120 days back and is labelled final, and a development-factor estimate for the immature months drawn as a dashed range and labelled an estimate.
  4. Hand over one sentence the executive can repeat without you in the room: the recent months look better because the disputes have not arrived yet, not because fewer will arrive.
  5. Offer a weekly signal they can watch instead, such as the risk-score mix of approved volume or the decline-rule hit rate, and state up front what it does and does not predict.
Follow-up
  • The deck ships tomorrow regardless. What exactly goes on the slide, and what wording do you insist on?
  • How would you estimate the development factors, and how would you notice if they had shifted?

Turn a one-line fraud-number request into a scoped brief

easy
scopingmetric definitiondenominators

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
  1. 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.
  2. Offer a short menu rather than an open question: a stakeholder can choose between two named options but cannot specify a denominator from scratch.
  3. 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.
  4. 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.
  5. 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?
  • 01

    How do you handle feedback and criticism?

  • 02

    A finance lead is looking at first-chargeback rate by transaction month, built from fct_card_dispute joined to fct_payment_authorization on auth_id and attributed to requested_at. The last three months slope sharply down and the lead wants to announce a fraud improvement at tomorrow's review. Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, so those months are not complete. In five minutes, with no statistics vocabulary, explain why the decline is not yet evidence and say exactly what you would put on the slide instead.

  • 03

    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.

PracHub interview preparation framework ↗
Is this an official PNC Financial Services Group interview guide?

No. It is PracHub's own research and practice material for the Data Scientist role at PNC Financial Services Group. Rounds and questions reflect what candidates have reported, not a process PNC Financial Services Group has published, and they change over time. Confirm the current format and scope with your recruiter.

PracHub interview research ↗
What is the typical interview difficulty, and how much preparation time is advisable?

The interview difficulty for the Data Scientist role at PNC Financial Services Group is generally considered average, with candidates typically spending 2-4 weeks preparing. Focus on brushing up on both technical skills and behavioral questions.

PracHub interview research ↗
What differentiates successful candidates?

Successful candidates often demonstrate a strong blend of technical expertise, problem-solving abilities, and effective communication skills. Additionally, showing alignment with PNC's values of inclusivity and collaboration can set you apart.

PracHub interview research ↗
Can you describe the company culture and working style?

The culture at PNC Financial Services Group is inclusive and collaborative, emphasizing teamwork and innovation. Employees are encouraged to share ideas and contribute to a supportive environment that fosters professional growth.

PracHub interview research ↗
What is the typical timeline from initial screen to offer?

The timeline can vary but generally takes 1-3 weeks from the initial screening to receiving an offer, depending on the number of candidates and scheduling logistics.

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.