As a Data Scientist at Solytics Partners, you are at the forefront of the firm’s commitment to financial integrity. You will develop models designed to detect and prevent complex fraudulent activities, directly influencing the security and compliance posture of Solytics Partners' financial service clients. Your work is not merely academic; it is high-impact, requiring you to bridge the gap between advanced statistical modeling and real-world regulatory requirements.
The environment at Solytics Partners is defined by its focus on AML (Anti-Money Laundering) and Fraud Detection. You will work with large, complex datasets to identify subtle patterns of suspicious behavior, requiring both deep technical proficiency in Python or R and a strong grasp of domain-specific financial risks. This is a role for professionals who thrive on technical rigor and are motivated by the challenge of solving high-stakes problems in the financial sector.
Solytics Partners values candidates who can articulate the 'why' behind their models. Be prepared to explain how your analytical approach directly mitigates specific financial risks.
Initial Screening
reportedWhoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.
What to demonstrate
- Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
- Whether your reason for wanting the role points at the work itself rather than the company's reputation
- Whether your language signals the level being screened for: what you decided yourself versus what you were handed
How to prepare
- Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
- Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
- Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands
Technical Assessment
reportedMuch of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.
What to demonstrate
- Whether the query you write matches the plan you just described
- What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
- Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly
How to prepare
- Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
- Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
- Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock
Technical Interviews
reportedBefore 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 editorial advice for the preparation topics above.
Recalibrating an underwriting cutoff on approved and funded applicants only
Rejected applicants have no repayment outcome, and they were rejected because the incumbent model scored them badly, so the missingness depends directly on the outcome being modelled. Reject inference by augmentation or parcelling fills the gap using the incumbent model's own assumptions, which means it can confirm those assumptions but cannot test them. The only genuinely new information about the reject region comes from bureau performance on rejects who borrowed elsewhere, or from a deliberately randomised approval band around the cutoff.
Reading the most recent months of fraud and dispute rates as final
Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, and several reason codes run considerably longer, so the disputes belonging to a recent transaction month have simply not been filed yet. Any chart attributed by transaction date therefore slopes down at the right edge regardless of what is happening. The fix is to report only matured cohorts, or to apply development factors estimated from completed months and to show the estimate as an estimate.
Building features from data that postdates the prediction time
Check every feature against the timestamp at which the model would actually score, and drop anything computed from a window that includes or follows the label event. For a forecasting use case, split train and test by time rather than at random, and split by entity when the same entity recurs.
Dropping rows with missing values without naming the mechanism
Say whether the values are missing at random, missing by a known process, or missing in a way that depends on the outcome, and handle them accordingly. Deleting incomplete rows silently redefines the population whenever missingness correlates with what you are measuring.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How would you approach a fraud detection problem given a highly imbala…
How would you approach a fraud detection problem given a highly imbalanced dataset?
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Sanity-check the answer against a simple bound or a simulated case.
- Translate the result into the decision it informs, in one plain sentence.
Follow-up
- How would you explain this result to someone who does not know statistics?
- What sample size would you need to detect an effect half this size?
Explain the difference between supervised and unsupervised learning in…
Explain the difference between supervised and unsupervised learning in the context of fraud.
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Write down the assumption the method needs before you use the method.
- Sanity-check the answer against a simple bound or a simulated case.
Follow-up
- What sample size would you need to detect an effect half this size?
- Which assumption here is most likely to be violated in practice?
Explain the underlying intuition behind common machine learning algori…
Explain the underlying intuition behind common machine learning algorithms.
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Set a baseline first, so any model has something honest to beat.
- Say how the offline result would be validated online before it is trusted.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- Where could label leakage enter this setup?
What metrics would you use to evaluate a model designed to catch money…
What metrics would you use to evaluate a model designed to catch money laundering?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Check what information would not exist at prediction time, and exclude it.
- Say how the offline result would be validated online before it is trusted.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- Where could label leakage enter this setup?
Collapse retry chains and compute a dollar-weighted approval rate
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
- 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.
- 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.
- 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.
- 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.
- 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
- Filter out reversals and zero-amount rows, then sort by the chain key and requested_at.
- 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.
- Join the exponent and daily rate tables, compute value_reporting, and assert no nulls remain after the join.
- 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.
- Take 7-day rolling sums of both columns and divide, then confirm one hand-picked group-day against a direct filter.
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?
How would you reverse a string without using built-in functions?
How would you reverse a string without using built-in functions?
Approach
- 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.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
Implement a solution to sort an array without using the standard `.sor…
Implement a solution to sort an array without using the standard .sort method.
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- 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.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
What are the time and space complexity implications of your proposed s…
What are the time and space complexity implications of your proposed solution?
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
Vintage ninety-plus rate at twelve months on book
From fct_loan_performance_monthly, build a vintage table keyed on origination_month: the share of each cohort that ever reached days_past_due of 90 or more, or charge_off_flag = true, at or before months_on_book = 12. Restructuring resets days_past_due, so for any loan with restructured_flag true, evaluate only the month ends strictly before its first restructured month. Return origination_month, loans_funded, bad_loans and bad_rate. Exclude any cohort that does not yet have a months_on_book = 12 observation for every loan still on book.
Approach
- Establish the cohort denominator from the first month end each loan appears at, months_on_book = 0, so a loan is counted once in its origination_month rather than once per monthly row.
- Find each loan's first restructured month with MIN(as_of_month_end) FILTER (WHERE restructured_flag) OVER (PARTITION BY loan_id), or the equivalent grouped subquery, and keep it null for loans never restructured.
- Flag a loan bad if any row with months_on_book <= 12 and as_of_month_end earlier than that first restructured month has days_past_due >= 90 or charge_off_flag = true, which is what 'pre-restructure worst state' means in practice.
- Gate maturity by requiring the cohort's newest month end to be at least 12 months after origination_month, and report immature cohorts as incomplete rather than letting them appear at a flattering low rate.
- Aggregate to one row per origination_month and read the column downward, not across calendar time, because the whole point is comparing cohorts at equal age.
Worked solution 35 min
- CTE 1: per loan_id, derive origination_month, the first restructured month end, and the maximum months_on_book observed.
- CTE 2: join back to the monthly rows, filter to months_on_book <= 12 and to month ends before any restructure, then flag bad with a BOOL_OR.
- CTE 3: aggregate to origination_month with COUNT() as loans_funded and COUNT() FILTER (WHERE is_bad) as bad_loans.
- Apply the maturity gate and compute bad_rate with a numeric cast.
Follow-up
- Should a restructure inside 12 months count as bad in its own right? Argue both sides and say what you would actually ship.
- A loan that prepaid in full at month 4 never had a chance to go 90 days past due. In or out of the denominator, and why?
- The 2025-11 cohort is two points worse at month 12 than its neighbours. What three queries do you run before you call it a credit-quality change?
Can you discuss the trade-offs between different models for anomaly de…
Can you discuss the trade-offs between different models for anomaly detection?
Approach
- 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.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
Size a two-week test on the ecommerce authorization path
Your team wants to test a new decline-retry policy on the ecommerce channel. The metric is the dollar-weighted authorization approval rate from fct_payment_authorization: approved amount_minor over attempted amount_minor, after collapsing retry chains within a 15-minute window on (card_token_id, merchant_id, amount_minor) and excluding is_reversal rows and zero-amount verifications, all converted to one reporting currency. Baseline is 87 percent on roughly 900,000 collapsed attempts in two weeks. Randomisation is by customer_id, mean six attempts per customer. Give the minimum detectable effect at 80 percent power, two-sided alpha 0.05, and say what you would change if it is too large.
Approach
- Compute the count-weighted binomial anchor first, because it is the number everyone expects and you need it to show why it is wrong. With n = 450,000 attempts per arm and p = 0.87, MDE = (1.96 + 0.8416) * sqrt(2p(1-p)/n).
- State that the real metric is a ratio of two sums with a random denominator, not a Bernoulli mean, so the binomial standard error is not the right one. Linearise: Var(R) is approximately Var(Y_i - R * D_i) / (n * Dbar^2), where Y_i and D_i are the approved and attempted amount totals for customer i, n is the number of customers, and Dbar is the mean attempted amount per customer.
- Apply the clustering correction, since assignment is by customer and attempts within a customer are correlated. Ask for or estimate the intra-customer correlation; the design effect is 1 + (m - 1) * rho with m = 6, so rho = 0.05 gives 1.25 and inflates the MDE by sqrt(1.25) = 1.118.
- Do not assume the amount-weighted variance; estimate it by bootstrapping customers from the last eight weeks of history and resampling whole customers, which captures both the skew in amount_minor and the within-customer correlation in one step.
- If the MDE exceeds the plausible effect, list the levers in order of cost: extend duration (MDE falls as 1/sqrt(n)), restrict to the segment where the rule can bind at all rather than diluting across all traffic, apply CUPED on the customer's pre-period approved amount, or switch the primary to the count-weighted rate and demote the dollar-weighted rate to a secondary read.
Worked solution 20 min
- Split 900,000 collapsed attempts into 450,000 per arm and compute the binomial anchor: sqrt(2 * 0.87 * 0.13 / 450000) = sqrt(5.027e-7) = 7.090e-4; multiply by 2.8016 to get 1.99e-3.
- Derive the customer count: 900,000 / 6 = 150,000 customers, 75,000 per arm, and note that this is the true sample size for inference.
- Apply the design effect at rho = 0.05: 1 + 5 * 0.05 = 1.25, so the count-weighted MDE becomes 1.99e-3 * 1.118 = 2.22e-3.
- State that the dollar-weighted MDE requires the linearised variance and cannot be derived from p alone; specify the customer-level bootstrap that would produce it and note it will be larger because amount_minor is right-skewed.
Follow-up
- The rule only changes behaviour on declined attempts, which are 13 percent of traffic. How does restricting the analysis population to attempts that could have triggered the rule change both the MDE and the estimand?
- How would your sizing change if the split were 90/10 instead of 50/50, and why is the loss more than proportional?
- Currency: the metric sums amounts converted to one reporting currency. Would you use settlement_fx_rate or a rate table pinned at test start, and what does the choice do to variance?
Approval rate rose in every band yet fell overall
Monthly application approval rate on fct_loan_application fell from 62 to 57 percent. Cut by bureau_score band, the rate rose in every band, including the null-bureau band. Columns: application_id, channel, submitted_at, requested_amount_minor, declared_annual_income_minor, bureau_score, model_pd_12m, model_version, policy_rule_hits, decision, decided_by, decision_at. The denominator is decision in ('approve','decline'). Explain the arithmetic, quantify how much of the five-point fall is mix versus within-band movement, and say what you would tell the team that owns acquisition.
Approach
- Confirm the paradox is real rather than a banding artefact. Rebuild the bands on fixed cutpoints taken from the earlier period, because quantile bands re-cut each month move with the population and can manufacture this pattern on their own.
- Compute the exact decomposition rather than describing it: within = sum of w_i0 * (r_i1 - r_i0), mix = sum of r_i0 * (w_i1 - w_i0), interaction = sum of (w_i1 - w_i0) * (r_i1 - r_i0). The three terms sum identically to the change in the blended rate, so the report can state the split.
- Attribute the weight change by cutting the same fixed bands by channel. Keep null bureau_score as its own band; a thin file is a population signal, not missing data to be imputed away.
- Check whether the new arrivals also changed the population inside a band, by comparing requested_amount_minor and declared_annual_income_minor distributions within one band across the two months.
- Deliver two numbers rather than one story: policy is looser in every band, and the funnel is being fed a different population. Those have different owners and different fixes.
Follow-up
- If the new channel is profitable at its own approval rate, is the blended fall a problem at all?
- How would you present this so that nobody reads the blended series unaccompanied again?
- What breaks if you fix the mix by reweighting to a frozen band distribution every month?
Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Breadth pass: query fluency
- Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
- For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
- Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.
Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Breadth pass: statistics and inference
- Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
- Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
- Rewrite the two weakest answers the following morning from memory in full sentences.
Deliverable: Ten graded answers with an honest count of exact hits.
Practice prompt ↗Practice prompt ↗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.
Saying no well is a senior skill and it is rarely rehearsed. Think of a time you told someone their analysis was not worth doing, or that the experiment could not answer their question at the sample size available. Explain what you offered instead. Refusal without an alternative reads as obstruction rather than judgement.
How do you explain a complex model to a non-technical stakeholder?
How do you explain a complex model to a non-technical stakeholder?
Approach
- Quantify the outcome, including what you would not claim credit for.
- State the situation in two sentences and spend the rest on your reasoning.
- Pick a story where you drove the decision, not one where you observed it.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
Disagree with a product manager over an approval-rate target
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
- 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.
- 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.
- 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.
- 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.
- 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?
State honestly what your cutoff change actually contributed
Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.
Approach
- Define the counterfactual before computing anything: the claim is not what happened after the change, it is what would have happened had the old cutoff scored the same applications, which means replaying the old threshold on the post-change population.
- Build the swap set: applications the new cutoff approves that the old one declined, applications the old one approved that the new one declines, and everyone else held out as unaffected. Only the swap groups carry your effect. Note that the swap-out group has no outcome under the new rule, because those loans were never funded, so its forgone margin must be estimated from matched pre-change approvals rather than observed.
- Price each swap group at months_on_book equal to 12 on the measure the cutoff was meant to move: interest and fees collected, minus net charge-offs, minus funding cost at the internal transfer rate.
- Strip the confounders explicitly. Cohorts affected by the bureau attribute change are either recomputed on the old attribute or excluded; channel mix is held fixed by reweighting to the pre-change mix; funding cost is charged at the rate in force each month rather than one blended average.
- State the residual you will not claim, with its size, and give a range rather than a point wherever cohorts have not yet reached 12 months on book.
Follow-up
- The swap-in group is only 6 percent of applications. How does that change the way you present the number, and to whom?
- What would you have needed to set up at launch to make this attribution clean, and why was a randomised band around the cutoff not used?
- 01
How do you explain a complex model to a non-technical stakeholder?
- 02
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.
- 03
Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.
Is this an official Solytics Partners interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Solytics Partners. Rounds and questions reflect what candidates have reported, not a process Solytics Partners has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How long does the entire interview process take?
While it can vary based on scheduling, the process is generally thorough and can span several weeks due to the multiple technical rounds.
PracHub interview research ↗What is the best way to prepare for the coding rounds?
Focus on medium-level DSA questions, particularly those involving strings, arrays, and sliding window concepts. Practicing live coding while explaining your thought process is highly recommended.
PracHub interview research ↗How should I handle a disagreement with an interviewer?
Stay calm and professional. Use data and logical reasoning to explain your stance. Candidates report that interviewers respond well to people who defend their work based on facts.
PracHub interview research ↗Are there specific domain areas I should focus on?
Yes, emphasize your understanding of AML regulations and common fraud patterns in banking or financial services.
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
Explore related engineering environments and compare the preparation focus.
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