Axos Clearing · Data Scientist
Updated · 2026-09-22

Axos Clearing Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

As a Data Scientist at Axos Clearing, you play a pivotal role in harnessing data to drive strategic decision-making and enhance business outcomes. Your expertise will directly influence key initiatives related to revenue generation, artificial intelligence, and data monetization, underscoring the importance of data-driven insights in shaping the company's future. This role is integral to the development of innovative products and services that enhance user experience and optimize operational efficiency.

A large share of questions open as "how would you measure X", where the real work is choosing the metric, fixing its denominator, and defining the population it applies to. Any computation comes last and is frequently not required at all.

Axos Clearing candidates report 2 rounds · ≈ 2-4 weeks. The stages below are what candidates describe, not a published process.

Pick a randomisation unit that respects interferenceDefine numerator, denominator and window preciselySize an experiment before anyone launches it

31 min read

Browse Data Scientist questions

See the practice prompts

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

As a Data Scientist at Axos Clearing, you play a pivotal role in harnessing data to drive strategic decision-making and enhance business outcomes. Your expertise will directly influence key initiatives related to revenue generation, artificial intelligence, and data monetization, underscoring the importance of data-driven insights in shaping the company's future. This role is integral to the development of innovative products and services that enhance user experience and optimize operational efficiency.

The impact of your work will be felt across various teams and projects, from analyzing user behavior to developing predictive models that inform business strategy. You'll be tasked with solving complex problems, utilizing advanced methodologies, and collaborating with diverse teams to translate data into actionable insights. This position offers a unique opportunity to work in a dynamic environment that values innovation, making it a critical and exciting place for aspiring data scientists.

01

Phone Screening

reported

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

What to demonstrate

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

How to prepare

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

In-Person Interviews

reported

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

What to demonstrate

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

How to prepare

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

PracHub editorial advice for the preparation topics above.

01

Comparing cohort retention curves of different maturities, or building the curve from users who are still present

A cohort four weeks old has no week-8 value, so an average taken across cohorts silently drops young cohorts from the later columns and keeps them in the earlier ones. The curve then bends upward at the tail, and the reading that 'retention is improving over time' is an artefact of which cohorts survived to be measured. The same error appears in the denominator when retention is computed over users active in the current period rather than over the full original cohort, which conditions on survival and guarantees a flattering number. The fix is a triangle: fix the cohort at signup, bound every window on both sides, and only compare cells where every cohort has had the full elapsed time, publishing the rest as blank rather than as a partial average.

02

Slicing a flat experiment until a segment reaches significance

Testing one metric across twenty segments at a nominal 5% level produces a significant result about two thirds of the time when nothing is happening anywhere, and the segment that surfaces is by construction the one with the most favourable noise. The reported effect in that slice is then badly overstated, because selection on significance conditions the estimate on being large. What makes it dangerous rather than merely wrong is that a post-hoc segment always has a plausible story attached, so it survives the meeting. The controls are declaring the small number of segments of interest before launch, correcting across the ones tested, and treating anything discovered afterwards as a hypothesis that needs its own adequately-powered test rather than a finding.

03

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.

04

Sizing estimates built on unnamed, unrevisable assumptions

Write each assumption as a named number you can change, then show the arithmetic so the interviewer can challenge one input instead of the whole answer. Finish by saying which assumption the result is most sensitive to, which matters more than the point estimate.

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

13 technical prompts3 include a worked solution

Write a function to calculate the mean and standard deviation of a lis…

medium
statistics and probability

Write a function to calculate the mean and standard deviation of a list of numbers.

Approach
  1. Quantify uncertainty explicitly rather than reporting a point estimate alone.
  2. Write down the assumption the method needs before you use the method.
  3. 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?
  • Which assumption here is most likely to be violated in practice?

How would you implement a decision tree from scratch?

medium
machine learning and modelling

How would you implement a decision tree from scratch?

Approach
  1. Pick an evaluation metric that matches the cost of each error type, not a default.
  2. Frame the prediction: the label, the moment of prediction, and the action it triggers.
  3. Check what information would not exist at prediction time, and exclude it.
Follow-up
  • How would you choose the decision threshold, and who owns that choice?
  • Where could label leakage enter this setup?

Sessionise an event stream with gap and midnight rules

hardWorked solution
sessionisationvectorisationevent streams

Sessionise a raw event stream. Input: a DataFrame with visitor_id, user_id (often NULL), occurred_at_utc and event_name, unsorted, up to 5 million rows. A session breaks when the gap from that visitor's previous event exceeds 30 minutes, and is force-closed at UTC midnight so no session spans two calendar dates. A gap of exactly 30 minutes does not break. Emit one row per session with session_id, visitor_id, the user_id as of the last event in the session, started_at_utc, ended_at_utc, session_date, duration_seconds and event_count. Vectorise; do not loop per visitor.

Approach
  1. Sort by ['visitor_id', 'occurred_at_utc', 'event_id'] once, then express the whole problem as one boolean vector: a row starts a new session when the visitor changed, or the gap exceeds 30 minutes, or the UTC date differs from the previous row's UTC date. Cumsum that vector and you have the session key.
  2. Get the comparison direction right on the gap: the rule is strictly greater than 1800 seconds, so an event at exactly 1800 seconds continues the session. Write it as gap > pd.Timedelta(minutes=30), and make the tie a test case rather than an assumption.
  3. Derive the midnight break from the date change, not from inserting synthetic boundary rows. A date change implies a break even when the gap is two seconds, which is precisely the force-close rule and is why the two conditions are ORed rather than one subsuming the other.
  4. Aggregate with a single groupby on the session key: min and max of occurred_at_utc, size for event_count, and last for user_id, which is correct because the frame is already sorted so 'last' is the final event in the session. That is the identity-as-of-session-end rule.
  5. Compute duration_seconds as (max - min).dt.total_seconds(), which makes a single-event session 0 seconds. Say so explicitly, because a downstream mean session duration is sensitive to whether single-event sessions are 0 or excluded.
Worked solution 35 min
  1. df = df.sort_values(['visitor_id','occurred_at_utc','event_id']).reset_index(drop=True).
  2. new_visitor = df.visitor_id.ne(df.visitor_id.shift()); gap = df.occurred_at_utc.diff(); new_day = df.occurred_at_utc.dt.date.ne(df.occurred_at_utc.dt.date.shift()).
  3. is_start = new_visitor | (gap > Timedelta(minutes=30)) | new_day; df['session_key'] = is_start.cumsum().
  4. g = df.groupby('session_key'); out = g.agg(visitor_id=('visitor_id','first'), user_id=('user_id','last'), started_at_utc=('occurred_at_utc','min'), ended_at_utc=('occurred_at_utc','max'), event_count=('event_id','size')).
  5. out['session_date'] = out.started_at_utc.dt.date; out['duration_seconds'] = (out.ended_at_utc - out.started_at_utc).dt.total_seconds(); assign session_id from the sorted index.
EXPECTED RESULTOne row per session where session_count equals is_start.sum(), event_count sums exactly to len(input), every session's started_at and ended_at share one UTC date, and duration_seconds is at least 0 and strictly less than 86400.
Follow-up
  • Sessions are used as the denominator of a conversion rate. How does moving the inactivity gap from 30 to 45 minutes move that rate, and in which direction?
  • A visitor's clock is 40 minutes ahead, so their events arrive with future occurred_at values. What does your sessioniser do, and what would you rather it did?
  • The same person signs up mid-session on mobile and continues on desktop. How many sessions and how many users does your output show, and is that the right answer?

Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.

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

Prepare, practise & reflect

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

0 / 7 done
01Breadth pass: query fluency
  • Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
  • For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
  • Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.

Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.

Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗
02Breadth pass: statistics and inference
  • Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
  • Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
  • Rewrite the two weakest answers the following morning from memory in full sentences.

Deliverable: Ten graded answers with an honest count of exact hits.

Practice prompt ↗Practice prompt ↗Practice prompt ↗
03Breadth pass: modelling
  • Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
  • Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
  • Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.

Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.

Practice prompt ↗Practice prompt ↗
04Breadth pass: product judgement
  • Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
  • For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
  • Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.

Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.

Practice prompt ↗Practice prompt ↗Worked solution ↗
05Depth, first area
  • Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
  • Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
  • Re-solve the two you failed the same evening with notes closed.

Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.

Practice prompt ↗Practice prompt ↗
06Depth, second area, and the seam between them
  • Repeat the depth protocol on the second-ranked area with the same six-problem structure.
  • Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
  • Solve your own combined problem end to end and note where the handoff between the two areas cost you time.

Deliverable: One combined problem, solved end to end, with the handoff failure written down.

Practice prompt ↗Practice prompt ↗
07Integration and re-measurement
  • Re-run the six prompts from day one under the same clock and compare both correctness and time.
  • Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
  • Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.

Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.

Practice prompt ↗Practice prompt ↗Worked solution ↗

Expand any day for tasks and deliverables. Your progress is saved on this device.

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.

What motivates you in your work as a data scientist?

medium
behavioural and stakeholder questions

What motivates you in your work as a data scientist?

Approach
  1. Name the disagreement or constraint, and how you resolved it with evidence.
  2. Pick a story where you drove the decision, not one where you observed it.
  3. Close with what you would do differently, concretely.
Follow-up
  • What did you decide not to do, and why?
  • How did you know the outcome was caused by your change?

Handle a request for numbers supporting a decision already made

hard
integrityframingstakeholder

A senior leader has already decided to sunset a plan tier and asks you for the analysis showing it is the right call. Accounts on that tier carry 6% of MRR at constant FX and have the highest licensed-seat utilisation in the book. The leader's support matters to your next review cycle, and the decision is being presented in four days. Deliver what you produce, what you decline to produce, and the exact sentence you will say in the meeting where the number appears on a slide.

Approach
  1. Recognise what is being probed: whether you can find the legitimate request inside an illegitimate framing instead of either complying or refusing on principle. The generic answer promises to push back; the strong one produces something genuinely useful and states its limits in the room, without ambushing anybody.
  2. Separate the decision from the justification. Sunsetting the tier may be correct for reasons the data does not hold, such as support cost, roadmap surface area or sales motion. What you decline is a one-sided document. What you produce is the case read both ways, which also happens to be more useful to the leader.
  3. Build the symmetric analysis: MRR at risk at constant FX, the share of affected accounts with a plausible migration path given seats_licensed and billing_term, the recovery rate assumed for that migration and where it came from, and the downside case in which high-utilisation accounts treat the sunset as a reason to re-evaluate the vendor entirely.
  4. Surface the inconvenient fact privately and early. The highest seat utilisation in the book is a retention signal, and the leader should hold it before the room does, so they can incorporate it rather than be caught by it.
  5. Agree the meeting sentence in advance with the leader, so that nobody is surprised. Something to the effect that the tier is 6% of MRR and its accounts are the most heavily used in the book, and that the case for sunsetting rests on cost and focus rather than on revenue. That is true, it supports the decision on its real grounds, and it stops the deck claiming the numbers endorse it.
  6. Decide your own line before you need it: what you will not put your name to, and that the route if asked anyway is your own manager rather than a confrontation in the meeting.
Follow-up
  • The deck circulates with your analysis included and the downside case removed. What do you do, and by when?
  • What changes if the honest analysis says the sunset is clearly the wrong call?
  • How do you write the same memo when the leader is your skip-level and the meeting is tomorrow?

Explain a wide interval to a non-technical executive

medium
communicationuncertaintypricing

A pricing change is under consideration. Your best estimate of its effect on trial-to-paid conversion is a 1.8pp drop, with a 95% interval from a 4.6pp drop to a 1.0pp rise, read from a geo holdout rather than a randomised test. An executive preparing a board slide asks you for 'the number'. You have ninety seconds and one slide, and the words confidence interval, p-value and significance are not usable with this audience. Deliver the slide headline, the single supporting line, and what you say aloud.

Approach
  1. Recognise what is being probed: whether you can carry uncertainty into a decision instead of either hiding it or hiding behind it. The generic answer promises to explain the interval in plain English; the strong one replaces the question 'what is the number' with 'across this range, where does the decision change'.
  2. Find the threshold before you draft anything. Ask what the pricing case assumes, then compute the conversion drop at which the higher price stops adding revenue: price uplift on the conversions kept against the revenue lost from conversions forgone. That single figure is what makes the range legible.
  3. Restate the estimate and both bounds in the unit the audience already reasons in. Convert percentage points into monthly first-paid conversions at current trial volume, then into mrr_cents_constant_fx, so the slide reads as money per month rather than as statistics.
  4. Place the range against the break-even and say which part of it sits on each side. If most of the range clears the threshold, that is a recommendation to proceed with a monitoring plan; if the range straddles it, that is a recommendation to narrow the range first.
  5. Name what would narrow it and what that costs in weeks, then give one recommendation with an explicit condition for revisiting it. Uncertainty stated without a next step is read as indecision and the midpoint gets used anyway.
Follow-up
  • The executive says to give the midpoint and they will manage the risk. What do you do?
  • How does the slide change if the interval were a 4.6pp to 0.2pp drop, with no positive outcomes in range?
  • Why is a geo holdout the credible read here rather than the attributed channel numbers you already have?
  • 01

    What motivates you in your work as a data scientist?

  • 02

    A senior leader has already decided to sunset a plan tier and asks you for the analysis showing it is the right call. Accounts on that tier carry 6% of MRR at constant FX and have the highest licensed-seat utilisation in the book. The leader's support matters to your next review cycle, and the decision is being presented in four days. Deliver what you produce, what you decline to produce, and the exact sentence you will say in the meeting where the number appears on a slide.

  • 03

    A pricing change is under consideration. Your best estimate of its effect on trial-to-paid conversion is a 1.8pp drop, with a 95% interval from a 4.6pp drop to a 1.0pp rise, read from a geo holdout rather than a randomised test. An executive preparing a board slide asks you for 'the number'. You have ninety seconds and one slide, and the words confidence interval, p-value and significance are not usable with this audience. Deliver the slide headline, the single supporting line, and what you say aloud.

PracHub interview preparation framework ↗
Is this an official Axos Clearing interview guide?

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

PracHub interview research ↗
How difficult is the interview process at Axos Clearing?

The interview process is rigorous, focusing on both technical and behavioral aspects. Candidates typically find that thorough preparation in data science concepts and interpersonal skills is essential.

PracHub interview research ↗
What differentiates successful candidates?

Successful candidates demonstrate a strong grasp of both technical skills and the ability to communicate effectively. They show a passion for data and a collaborative mindset.

PracHub interview research ↗
What is the company culture like?

Axos Clearing fosters an innovative, data-driven culture that values collaboration and continuous learning. Expect a dynamic environment that encourages creativity and problem-solving.

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

The hiring process can take several weeks, depending on the number of interview stages and candidate availability. Stay proactive in communication throughout the process.

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.