Memorial Sloan Kettering Cancer Center · Data Scientist
Updated · 2026-09-24

Memorial Sloan Kettering Cancer Center Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

This guide covers what a Data Scientist at Memorial Sloan Kettering Cancer Center is expected to do and how to prepare for the interview.

If the team owns experimentation, expect depth past a two-sample test: minimum detectable effect and its roughly inverse-square-root dependence on sample size (holding power, significance level and variance fixed), variance reduction from pre-period covariates, interference between units, and when a sequential design is the right call.

Memorial Sloan Kettering Cancer Center candidates report 3 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Reconcile amounts in minor units and currencySet fraud thresholds by expected costSeparate authorization, settlement and dispute outcomes cleanly

33 min read

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See the practice prompts

17Practice promptsAcross five skill areas
3With worked solutionsIncluded in the practice prompts

This guide covers what a Data Scientist at Memorial Sloan Kettering Cancer Center is expected to do and how to prepare for the interview.

01

Technical Screening

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 ↗
02

Interviews with Staff

reported

An extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.

What to demonstrate

  • Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
  • Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
  • How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one

How to prepare

  • Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
  • Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
  • Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.
PracHub interview research ↗
03

Formal Presentation

reported

Rounds 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.
PracHub interview research ↗

PracHub editorial advice for the preparation topics above.

01

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.

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

Reaching for a model before the target metric exists

Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.

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.

14 technical prompts3 include a worked solution

How would you define a success metric for a new diagnostic algorithm?

medium
machine learning and modelling

How would you define a success metric for a new diagnostic algorithm?

Approach
  1. Set a baseline first, so any model has something honest to beat.
  2. Frame the prediction: the label, the moment of prediction, and the action it triggers.
  3. Say how the offline result would be validated online before it is trusted.
Follow-up
  • Where could label leakage enter this setup?
  • What would you monitor after launch to know the model is still valid?

Estimate a delinquency roll-rate matrix and project twelve months

hard
roll ratesmarkov chainsurvivorship

fct_loan_performance_monthly gives loan_id, as_of_month_end, months_on_book, delinquency_bucket, charge_off_flag, prepaid_in_full_flag and restructured_flag. Build a month-to-month transition matrix over the five delinquency buckets plus absorbing charged_off and prepaid states. Loans that stop appearing must be routed to an absorbing state rather than dropped. Project the current book forward 12 months by repeated matrix multiplication and report the projected share reaching charge-off. Handle restructured_flag explicitly, and name one place the Markov assumption fails on this data.

Approach
  1. Build consecutive month pairs per loan by shifting as_of_month_end within loan_id, then verify the shifted value is exactly one month later. A gap is not a transition, it is an exit you have not resolved yet.
  2. Resolve exits before counting anything. A loan whose last row carries charge_off_flag moves to charged_off, one carrying prepaid_in_full_flag moves to prepaid, and one that disappears with neither is a data question to raise rather than silently discard, because discarding it is survivorship that inflates every cure rate.
  3. Count pairs into a 7 by 7 matrix and row-normalise. Assert every row sums to one and the two absorbing rows are the identity; a row that does not sum to one means exits were dropped.
  4. Decide and state the restructure rule. Restructuring resets days_past_due, so a dpd_60_89 to current move on a restructured loan is not a cure. Either give restructured loans their own state or carry the pre-restructure bucket, but do not let that move land in the cure cell.
  5. Project by taking the current bucket distribution as a row vector and multiplying by the matrix twelve times. Report the charged_off entry, and report it again from an all-current starting vector so the reader can see how much of the projection comes from loans that are already delinquent today.
  6. State the homogeneity failure plainly: transition rates depend strongly on months_on_book, so one pooled matrix applied to a book with a young mix understates early-life delinquency. If the mix is moving, estimate separate matrices by seasoning band.
Follow-up
  • How would you validate the projection against what actually happened, and over what window?
  • The cure rate out of dpd_30_59 rose five points last quarter. What are the candidate explanations and how would you separate them?
  • When would you prefer a vintage curve to a roll-rate projection, and why?

Write integrity checks for the authorization and settlement lifecycle

easyWorked solution
data qualityminor unitsfx reconciliation

You are given fct_payment_authorization as a pandas DataFrame with auth_id, requested_at, amount_minor, transaction_currency, auth_result, decline_reason_code, is_reversal, parent_auth_id, captured_at, captured_amount_minor, settled_at, settlement_amount_minor, settlement_currency and settlement_fx_rate. Write a function returning one row per integrity check with the check name, failing row count, failing share and up to five example auth_id values. Cover at least six checks, one of which reconciles captured_amount_minor against settlement_amount_minor through settlement_fx_rate. Partial capture, zero-amount verification and a decline with no capture are all legitimate and must not be flagged.

Approach
  1. Separate contract violations from observations before writing any code: an approved row carrying a decline_reason_code is structurally impossible, while a capture two days after requested_at is merely slow and belongs in a different severity tier.
  2. Express each check as a boolean mask over the whole frame and collect the masks in a dict, so the summary table is one comprehension over mask.sum() rather than a row loop.
  3. For the reconciliation, leave minor units before comparing: expected = captured_amount_minor / 10exponent[transaction_currency] * settlement_fx_rate * 10exponent[settlement_currency]. Build the exponent table covering zero-decimal and three-decimal currencies instead of assuming two everywhere.
  4. Guard the legitimate cases explicitly so each mask fires only on the genuine contradiction: captured_amount_minor below amount_minor is partial capture, amount_minor of zero on an approved row is account verification, a null captured_at on a declined row is correct.
  5. Sort the output by failing share times a stated severity weight, because a check firing on 0.01 percent of rows can still be the one that breaks a ledger reconciliation.
Worked solution 25 min
  1. Assert auth_id is unique, then build a currency exponent lookup that includes the zero-decimal and three-decimal currencies present in the data.
  2. Define masks for: approved with non-null decline_reason_code; declined with non-null captured_at; captured_amount_minor above amount_minor with parent_auth_id null; captured_at before requested_at; settled_at before captured_at; is_reversal true with parent_auth_id null; settlement_currency differing from transaction_currency while settlement_fx_rate is null.
  3. Add the exponent-aware reconciliation mask with a tolerance of one minor unit plus a small relative term.
  4. Assemble a frame of check_name, n_failing, pct_failing and up to five sample auth_id values, ordered by severity then share.
  5. Read five flagged rows per check by hand and confirm each is genuinely contradictory before reporting any counts.
EXPECTED RESULTA DataFrame of at least eight rows, one per check, each with n_failing, pct_failing and example auth_id values. The reconciliation check should fire on a whole currency at once rather than on scattered rows, because an exponent error is systematic while an FX error is not.
Follow-up
  • Which of these would you run as a blocking pipeline assertion and which as a monitored metric, and why?
  • The FX check fails on 3 percent of rows, all in one settlement currency. How do you decide between a data bug and a rounding convention?
  • How would you detect that a currency's minor-unit exponent is wrong in your reference table, using only the transaction data?

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 ↗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.

Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.

Describe a time you took initiative to improve a data pipeline or rese…

medium
behavioural and stakeholder questions

Describe a time you took initiative to improve a data pipeline or research process that was inefficient.

Approach
  1. Name the disagreement or constraint, and how you resolved it with evidence.
  2. State the situation in two sentences and spend the rest on your reasoning.
  3. 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?

Allocate one analyst-week across three competing risk requests

medium
prioritisationdecision deadlinesstakeholder negotiation

Three requests land in the same week and you have one analyst-week. Payments wants a merchant-level decline teardown before a contract renewal in nine days. Credit wants a swap-set analysis on a cutoff change scheduled to ship in six weeks. Insurance wants accident-quarter loss ratios at 12 months development for a reserving review with no fixed date. Each sponsor believes theirs is first, and each has escalated before. Produce the allocation, the reasoning you would say out loud to all three at once, and what you explicitly drop.

Approach
  1. Score each request on the decision it unblocks rather than on effort or on how loudly it arrived: what changes if it is late, and is that change reversible.
  2. Separate deadline from value. The nine-day renewal is a hard, irreversible date with a bounded prize; the six-week cutoff has slack but a much larger downside if it ships unmeasured; the reserving number has no date but feeds external reporting, which is its own kind of hard.
  3. Hunt for the cheap partial in each: a decline teardown restricted to the top merchants by declined value usually answers the contract question at a fraction of the full cut.
  4. Sequence by hard date first, then by largest irreversible downside, and deliver the trade-off to all three sponsors in one message rather than three, so nobody negotiates privately against a version you told someone else.
  5. Name what is dropped and who now owns that consequence, in writing, so the trade-off is visible rather than silently absorbed by you.
Follow-up
  • The credit sponsor escalates to your manager. What do you change, and what do you refuse to change?
  • How would you make this allocation reproducible so the next contested week is a rule application rather than a negotiation?

Disagree with a product manager over an approval-rate target

medium
metric designdenominatorsinfluence without authority

A product manager proposes a quarterly goal of raising card authorization approval rate by 150 basis points, measured as approved authorizations divided by all authorizations in fct_payment_authorization. You believe that metric can be hit with no customer benefit, because merchant retry chains, zero-amount verification authorizations, incremental authorizations and reversals all sit in the denominator, and declines skew toward high-value cross-border ecommerce. You support the underlying goal. In one working session, change the metric without killing the initiative, and name the guardrail you would accept.

Approach
  1. Separate the goal from the metric out loud and agree with the goal first, so the disagreement stays narrow and technical rather than becoming positional.
  2. Demonstrate the failure rather than asserting it: compute the proposed metric and the dollar-weighted collapsed version over the same recent window, and find a period where they moved in opposite directions.
  3. Propose the replacement precisely: sum of approved amount_minor over sum of attempted amount_minor, after collapsing retries to one attempt per card_token_id, merchant_id and amount_minor within a 15-minute window, excluding is_reversal rows and zero-amount verifications, with everything converted to one reporting currency before summing.
  4. Attach the guardrail that makes the target honest: matured first-chargeback rate and net fraud loss in basis points of settled volume, read only on transaction months carrying at least 120 days of maturity.
  5. Give the product manager something back: the replacement metric cuts cleanly by channel and issuer_country, which makes a roadmap of merchant-specific and authentication fixes legible in a way the blended rate never was.
Follow-up
  • How do you identify a retry chain when the merchant varies the amount slightly between attempts?
  • The product manager wants a weekly read on the guardrail. What is the earliest defensible signal, and how do you label it?
  • 01

    Describe a time you took initiative to improve a data pipeline or research process that was inefficient.

  • 02

    Three requests land in the same week and you have one analyst-week. Payments wants a merchant-level decline teardown before a contract renewal in nine days. Credit wants a swap-set analysis on a cutoff change scheduled to ship in six weeks. Insurance wants accident-quarter loss ratios at 12 months development for a reserving review with no fixed date. Each sponsor believes theirs is first, and each has escalated before. Produce the allocation, the reasoning you would say out loud to all three at once, and what you explicitly drop.

  • 03

    A product manager proposes a quarterly goal of raising card authorization approval rate by 150 basis points, measured as approved authorizations divided by all authorizations in fct_payment_authorization. You believe that metric can be hit with no customer benefit, because merchant retry chains, zero-amount verification authorizations, incremental authorizations and reversals all sit in the denominator, and declines skew toward high-value cross-border ecommerce. You support the underlying goal. In one working session, change the metric without killing the initiative, and name the guardrail you would accept.

PracHub interview preparation framework ↗
Is this an official Memorial Sloan Kettering Cancer Center interview guide?

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

PracHub interview research ↗
How long should I spend preparing for the interview?

Given the technical nature of the role and the presentation requirement, most successful candidates spend 2–4 weeks preparing, focusing on both their past project narratives and technical fundamentals like SQL and statistics.

PracHub interview research ↗
What is the culture like at MSKCC?

It is a mission-driven, academic, and highly professional environment. Success is defined by both your technical contribution and your ability to collaborate across departments to advance cancer care.

PracHub interview research ↗
Will I be expected to know oncology-specific terminology?

While you do not need to be an oncologist, you should have a baseline understanding of the domain and demonstrate a high level of curiosity and interest in learning the medical context of your data.

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.