As a Data Scientist at Lpl Financial, you will operate at the intersection of complex financial data and actionable business strategy. The role is pivotal in transforming raw, high-volume data into insights that empower financial advisors and improve client outcomes. By leveraging advanced analytics, you will help the firm navigate the complexities of wealth management, risk assessment, and operational efficiency, directly influencing the strategic direction of the firm's products and services.
This position is inherently collaborative, requiring you to bridge the gap between technical rigor and executive-level decision-making. You will work within an environment that values precision and clarity, tasked with solving problems that have real-world implications for thousands of independent financial advisors. Whether you are designing experiments to measure product performance or building predictive models to optimize service delivery, your work will be foundational to Lpl Financial’s commitment to enabling financial independence.
The provided compensation data reflects the expected salary ranges for Data Scientist roles at Lpl Financial, accounting for market benchmarks and regional variations. Candidates should interpret these figures as a starting point for negotiation and recognize that total compensation packages typically include performance bonuses and benefits. Use this data to calibrate your expectations and ensure you are prepared to discuss your value proposition during the final stages of the process.
Recruiter Screen
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 Interviews
reportedThis round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.
What to demonstrate
- Whether your row counts survive each join, and whether you notice on your own when they do not
- Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
- Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
- Reaching a defensible answer inside the window instead of a refined one after it
How to prepare
- Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
- Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
- Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither
Behavioral Interviews
reportedRounds of this kind usually include one question about work that did not go well, and it is the part that carries the most information. Anyone can narrate a shipped win. What the interviewer learns from a project that stalled is how you behave without a result to hide behind: whether you noticed the problem yourself, how long it took, and who you told. Answers that route the failure onto a data pipeline or a reorganisation close the topic without answering it, and the follow-up comes back to your own part.
What to demonstrate
- Whether you found the error yourself or someone else found it, and how long it sat before anyone knew
- What you changed afterwards, stated as a check you now run rather than a lesson you now believe
- Whether the mistake you choose has real cost attached, such as a quarter of misdirected roadmap or a metric that was reported upward, instead of one that flatters you
How to prepare
- Choose a failure you caught yourself and be ready to say what tipped you off. A story where someone else caught it is still usable, but you will be asked why you missed it.
- Write down the check you added afterwards and where it lives now, so the correction is a concrete artefact rather than a resolution.
- Rehearse saying the cost out loud. Candidates shrink the number by instinct once the interviewer is in the room.
Final Interview Rounds
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
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.
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.
Defining the cohort on a post-treatment condition
Ask how rows entered the table. Filtering on something that treatment itself influences, such as users who finished onboarding or accounts still active at ninety days, breaks comparability between arms; define the population at an entry point that precedes exposure and keep everyone in it.
Extrapolating a first-week lift inflated by novelty effects
Plot the treatment effect by days since first exposure instead of quoting one pooled average. A lift that decays toward zero across the test window is behaviour that will not persist, and annualising it produces a forecast that misses by an order of magnitude.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
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.
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?
Estimate a delinquency roll-rate matrix and project twelve months
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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
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
- 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.
- 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.
- 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.
- 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.
- 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
- Assert auth_id is unique, then build a currency exponent lookup that includes the zero-decimal and three-decimal currencies present in the data.
- 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.
- Add the exponent-aware reconciliation mask with a tolerance of one minor unit plus a small relative term.
- Assemble a frame of check_name, n_failing, pct_failing and up to five sample auth_id values, ordered by severity then share.
- Read five flagged rows per check by hand and confirm each is genuinely contradictory before reporting any counts.
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?
Write a query using SQL window functions to calculate a three-month ro…
Write a query using SQL window functions to calculate a three-month rolling average for advisor activity.
Approach
- State the window function and its partition and ordering out loud before writing it.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Say which table is the grain you start from, and join outward from it.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Describe a situation where you had to optimize a complex, slow-running…
Describe a situation where you had to optimize a complex, slow-running query; what was your approach?
Approach
- Say which table is the grain you start from, and join outward from it.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- How does the query change if the join becomes one-to-many?
- What breaks if events arrive late or out of order?
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.
Worked solution 30 min
- CTE denom: SELECT date_trunc('month', requested_at) AS txn_month, COUNT(*) AS settled_txns FROM fct_payment_authorization WHERE settled_at IS NOT NULL AND NOT is_reversal GROUP BY 1.
- CTE numer: join fct_card_dispute to fct_payment_authorization on auth_id, filter dispute_stage to the four stages at or beyond first_chargeback, group by the same txn_month expression, count dispute rows.
- LEFT JOIN numer onto denom on txn_month so months with zero disputes appear as zero rather than disappearing.
- Derive the rate with a numeric cast and a status column driven by (txn_month + interval '1 month - 1 day') + interval '120 days' <= current_date.
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?
How do you balance long-term engagement metrics against short-term rev…
How do you balance long-term engagement metrics against short-term revenue goals in product development?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- Restate the decision this analysis has to support, and who acts on the answer.
- Fix the population and the time window before naming any metric.
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?
If we notice a sudden, significant drop in a key product metric, what …
If we notice a sudden, significant drop in a key product metric, what is your diagnostic framework for identifying the root cause?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- State what result would change your recommendation, so the answer is falsifiable.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How would you design a metric to measure the success of a new advisor-…
How would you design a metric to measure the success of a new advisor-facing dashboard?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- State what result would change your recommendation, so the answer is falsifiable.
- Fix the population and the time window before naming any metric.
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?
What are the most common experimentation pitfalls that lead to false p…
What are the most common experimentation pitfalls that lead to false positives in A/B testing?
Approach
- Decide the analysis before seeing data, including how long it runs and when you look.
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Say whether units interfere with each other, and switch design if they do.
Follow-up
- What would you do if you could not randomise at all?
- How would you handle interference between treated and control units?
How do you determine the appropriate sample size for an A/B test when …
How do you determine the appropriate sample size for an A/B test when the effect size is expected to be small?
Approach
- State the primary metric and the minimum effect worth shipping, then size the test.
- Name the guardrails that would stop a launch even on a positive primary result.
- Name the randomisation unit first; it decides the variance and what the test can detect.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
Build the metric tree for a new instalment loan
A new instalment loan product launches next quarter. Build the metric tree from fct_loan_application and fct_loan_performance_monthly: one primary metric, the intermediate metrics that explain it, and the guardrails. The product owner wants a go or no-go call six weeks after the first disbursements, while the primary outcome is defined at twelve months on book. Name the leading indicator you would accept for the six-week call, state its bias in one sentence, and say what you would refuse to conclude from it.
Approach
- Put the primary where the trade is real: risk-adjusted margin per 1,000 units of principal originated, evaluated at months_on_book = 12, charging funding cost at the internal transfer rate. Approval rate and origination volume can both be moved in a week by lowering the cutoff, and neither charges anything for the consequence.
- Lay the tree out by layer so a movement can be localised: approval rate on a decision-reaching denominator (decision in ('approve','decline'), excluding withdrawn and expired) feeds offer-to-funded conversion within 30 days by offered_apr_bps band, which feeds the vintage 90+ rate at months_on_book = 12, which feeds risk-adjusted yield on average balances. Each layer's denominator is the numerator of the layer above.
- Choose guardrails that catch the opposite failure to the primary's. A margin target is trivially met by shrinking the book to its safest slice, so report approval rate within each model_pd_12m band, where that collapse shows up first, alongside adverse-impact monitoring of the decision rule under compliance review.
- For the six-week call, accept first-payment behaviour: the share of funded loans missing the first scheduled payment, or reaching days_past_due at or above 30 by months_on_book = 3. Estimate its relationship to the 12-month 90+ rate from completed vintages of the nearest comparable product, and report the dispersion of that ratio across those vintages as the uncertainty rather than quoting a point estimate.
- State the bias in one sentence: early default is loaded toward application fraud and income misstatement and is close to blind to affordability deterioration that develops over months, which is the failure mode a new product with a new population is most likely to have, and the historical ratio breaks exactly when the population or the macro environment changes.
- Say what six weeks cannot answer: nothing about the 12-month level in absolute terms, and nothing whatsoever about the applicants the policy declined.
Worked solution 35 min
- Write each metric in the tree as numerator, denominator, exclusions and window, four lines each, before writing any SQL.
- Build a vintage table from fct_loan_performance_monthly with origination_month as rows and months_on_book as columns for an existing comparable product, and read the diagonal as calendar time.
- For the last eight to twelve complete vintages, compute both the 30+ rate at months_on_book = 3 and the 90+ rate at months_on_book = 12, take the ratio, and record its minimum, median and maximum.
- Write the six-week readout using that ratio range as an interval, with the one-sentence bias statement attached to the number itself rather than to a footnote.
Follow-up
- The first vintage looks excellent at three months on book. What would make you distrust it?
- How do restructured loans enter your twelve-month numerator?
- Which metric in this tree would you refuse to put on a weekly dashboard, and why?
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?
Roughly 90 minutes a night on weekdays with one longer weekend block. The plan deliberately cuts scope rather than compressing everything, on the assumption that finishing one thing a night beats half-starting four.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix the scope and set a baseline
- Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list for everything else and keep it visible all week.
- Take one 20-minute SQL prompt and one 10-minute metric question cold, and write the single sentence that says what blocked each attempt, since that sentence is what decides which two topics get the most evenings.
- Set the week's one rule: one problem finished to completion every night, including the night you only have 40 minutes.
Deliverable: A one-page scope with an explicit not-doing list and two cold attempts, each carrying one sentence on what blocked it.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02One query pattern, written three times
- Choose the single pattern most likely to appear (a cohort retention grid, or a funnel counted by user) and write it three times from a blank file rather than editing the previous attempt.
- On the third attempt, write the grain of every CTE as a comment before writing its body.
- Stop at 90 minutes even if the third version is imperfect, and write the one thing you would fix with another hour.
Deliverable: Three independent versions of the same query plus a note on what changed between them.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Only the statistics you will be asked to defend
- Write, in under 200 words, how you would decide whether a difference between two groups is real: the test, its assumptions, and what you would switch to when an assumption fails.
- Compute a 95 percent confidence interval for a difference in proportions by hand on realistic numbers, then write in one sentence what changes if the two samples are paired rather than independent.
- Write your answer to "what does a p-value mean", check it against a definition, and delete the version that describes it as the probability the hypothesis is true.
Deliverable: A 200-word written answer and one hand-computed interval you can reproduce under pressure.
Practice prompt ↗Practice prompt ↗04One case, and the assumptions holding it up
- Answer one product case aloud in 20 minutes with a recording running, then listen back with a pen and mark every claim you asserted without saying what it rested on: an assumed user behaviour, an assumed data source, an assumed baseline rate, an assumed grain.
- Pick the three assumptions the recommendation actually depends on, write how you would check each one against data, and say which one being wrong would flip the recommendation rather than merely weaken it.
- Write the four-step structure you used onto a card small enough to hold in working memory when you are nervous.
Deliverable: One recording, three load-bearing assumptions each with a written check, and a four-step structure card.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Your own work, timed
- Write a 90-second version and a four-minute version of your main project, and time both out loud rather than reading them.
- Prepare answers to the two follow-ups that always come: what you would do differently, and how you knew it worked.
- Put one number in the first sentence and be able to say exactly where that number came from and what it excludes.
Deliverable: Two timed narratives with one defensible number in the opening line.
Practice prompt ↗Practice prompt ↗06The one full rehearsal, in a longer weekend block
- Run a 60-minute mock covering query work, a case and a behavioural question in a single sitting with no breaks, because sustained attention is the thing evenings have not trained.
- Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
- Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.
Deliverable: Mock notes naming three failure moments with a specific fix written under each.
Practice prompt ↗Practice prompt ↗07Taper
- Write the 20-minute warm-up you will actually do on the morning of the interview: one query you can already write from a blank file, one metric you can define out loud, and nothing you have never seen before.
- Re-read only your own notes from this week, and open no new material.
- Write down the logistics: the tool you will be asked to work in, whether lookups are allowed, and the sentence you will use when you do not know something.
Deliverable: A one-page card holding the case structure, the project numbers, and the logistics.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
An answer without a quantity is hard to interrogate, so interviewers keep probing until they find one. Come with the baseline, the change, the window it was measured over, and how confident you were. If the effect never got measured, say so and say what you would have measured. Fabricated precision is worse than an honest gap.
Explain the concept of statistical significance to a non-technical sta…
Explain the concept of statistical significance to a non-technical stakeholder.
Approach
- Quantify the outcome, including what you would not claim credit for.
- 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
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
Defend a vintage finding that contradicts the portfolio dashboard
The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.
Approach
- Reconcile before you contradict: show that aggregating your vintage table along the calendar diagonal reproduces the published blended series, so the disagreement is about age mix rather than about data quality.
- Make the mechanism arithmetic rather than rhetorical: a loan cannot reach 90 days past due before it is 90 days old, so rapid origination growth shifts weight onto young months-on-book where the rate is structurally near zero.
- Show every vintage rather than a selected pair, all indexed at months_on_book equal to 12, with cohort sizes printed beside each curve so nobody can claim the divergence rests on a thin cohort.
- Handle restructuring explicitly, because restructured_flag resets days_past_due: count each loan on its worst pre-restructure state, or recent vintages will look better than they are.
- Close on the decision rather than the chart: state what the divergence implies for the cutoff or the channel mix, and state in advance what evidence would make you withdraw the claim.
Follow-up
- Two cohorts differ at month 12. How do you separate a seasoning effect from a genuine credit-quality effect?
- Someone argues the recent vintages are simply a broker-channel mix shift. How do you test that, and what would confirm it?
Retract a published number after finding a currency bug
Two weeks ago you published an interchange and fraud analysis that summed amount_minor across fct_payment_authorization without converting currencies. Minor units are not two decimals everywhere: some currencies carry none and some carry three, so the sum has no interpretation. A pricing decision is already in flight on the back of it. You now have corrected figures. Produce the retraction: what you send, to whom, in what order, and what you change in the process so this class of error is caught next time rather than trusted next time.
Approach
- Size the error before announcing it, because saying the number is wrong without a magnitude and a direction forces every reader to assume the worst case.
- Check whether the conclusion actually flips: if the ranking that drove the pricing decision is unchanged, that belongs in the first sentence beside the correction rather than buried at the end.
- Tell the person acting on it first and directly, then the wider distribution, using the same text, so nobody learns about it secondhand.
- Write the correction as four parts: the old number, the cause in one clause, the effect on the pending decision, and the new number. Leave out self-flagellation, which makes the reader do emotional work instead of acting.
- Fix the class rather than the instance: a rule that a sum over amount_minor either groups by transaction_currency or passes through both conversion steps, exponent scaling and then a dated rate into one named reporting currency, plus a standing reconciliation of the settled subset to the settlement ledger inside each settlement_currency.
Follow-up
- The corrected figures do not change the decision. Do you still send the correction, and what does that choice signal?
- What automated check would have caught this, where would it live, and what would it cost in false alarms?
- 01
Explain the concept of statistical significance to a non-technical stakeholder.
- 02
The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.
- 03
Two weeks ago you published an interchange and fraud analysis that summed amount_minor across fct_payment_authorization without converting currencies. Minor units are not two decimals everywhere: some currencies carry none and some carry three, so the sum has no interpretation. A pricing decision is already in flight on the back of it. You now have corrected figures. Produce the retraction: what you send, to whom, in what order, and what you change in the process so this class of error is caught next time rather than trusted next time.
Is this an official Lpl Financial interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Lpl Financial. Rounds and questions reflect what candidates have reported, not a process Lpl Financial 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 technical rounds?
Most candidates benefit from 2–3 weeks of focused practice. Prioritize refreshing your knowledge of SQL syntax and reviewing core statistical concepts like hypothesis testing and power analysis.
PracHub interview research ↗What differentiates a great candidate from a good one?
A great candidate links their technical answer back to the business impact. They don't just solve the equation; they explain why that solution matters to the advisor or the firm.
PracHub interview research ↗Is the culture at Lpl Financial very formal?
Candidates report that the interviews weigh professionalism and clear communication. Interviewers respond well to candidates who are respectful, collaborative, and able to maintain a composed, analytical mindset even under pressure.
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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