PrizePicks · Data Scientist
Updated · 2026-09-22

PrizePicks Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

As a Data Scientist at PrizePicks, you play a pivotal role in shaping data-driven decision-making processes that enhance user experiences and optimize product offerings. Your analytical skills will directly influence the strategic direction of our daily fantasy sports platform, impacting everything from user engagement to revenue generation. In a rapidly evolving market, the insights you provide will not only help us stay competitive but also drive innovation in how we understand and interact with sports analytics.

Coding rounds for this role are usually data-manipulation shaped rather than data-structure shaped: group-bys, joins, time windows, ranking within a partition. Confirm the format before spending a week on graph traversal.

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

Define numerator, denominator and window preciselySize an experiment before anyone launches itSeparate novelty effects from durable behaviour change

33 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 PrizePicks, you play a pivotal role in shaping data-driven decision-making processes that enhance user experiences and optimize product offerings. Your analytical skills will directly influence the strategic direction of our daily fantasy sports platform, impacting everything from user engagement to revenue generation. In a rapidly evolving market, the insights you provide will not only help us stay competitive but also drive innovation in how we understand and interact with sports analytics.

This position is unique due to its intersection of sports enthusiasm and data science. You will be involved in diverse projects that leverage statistical modeling and machine learning techniques to analyze performance metrics, predict outcomes, and optimize user engagement. The collaborative environment at PrizePicks fosters a culture of innovation, making your contributions crucial to both the team and the broader business objectives.

Candidates can expect to work with cutting-edge technologies and methodologies while engaging with passionate colleagues who share a commitment to excellence. The role is not just about crunching numbers; it’s about storytelling through data, making it both challenging and rewarding.

01

Phone Screen

reported

Most candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.

What to demonstrate

  • Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
  • Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
  • The substance of the questions you ask back, which an experienced screener reads as a level signal

How to prepare

  • Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
  • Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
  • Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered
PracHub interview research ↗
02

Technical Interview

reported

Much 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
PracHub interview research ↗
03

Take-home Assessment

reported

The clock is part of the test. Three to six hours is not enough to do everything the dataset supports, so the submission mostly reveals how you spend a fixed budget against an open question. A reviewer sees which paths you took and, by absence, which you abandoned. Work that runs out of time inside the analysis ships a thin conclusion, while work that cuts scope early protects the last hour for writing. The most reliable way to lose here is to leave the scoping decision implicit, so it reads as something you missed rather than something you chose.

What to demonstrate

  • Whether the scope you settled on is presented as a decision with a reason, rather than left for the reader to infer from what is missing
  • Whether the depth of the work is consistent with the stated time budget, instead of several half-finished directions left open
  • Whether the closing section reads as something written on purpose rather than assembled from whichever cells survived

How to prepare

  • Run a timed rehearsal on a public dataset with a hard stop, holding the final sixty minutes for writing no matter where the analysis has got to
  • Before opening the data, list the questions it could plausibly answer, pick one, and keep the discarded ones as a short note on what you did not attempt and why
  • Commit a one-line finding after each analysis step so the writeup is assembled from recorded results rather than from memory at midnight
PracHub interview research ↗

PracHub editorial advice for the preparation topics above.

01

Watching an experiment daily and stopping when it crosses significance

A fixed-sample test controls type I error at one pre-declared look. Checking repeatedly and stopping at the first p < 0.05 inflates the false positive rate to roughly 0.15 to 0.20 for ten looks, and it rises further with more frequent checks, because the p-value takes a random walk that will eventually dip below the threshold under the null. The usual defences are a fixed horizon declared before launch, group-sequential boundaries such as O'Brien-Fleming that spend alpha across a planned number of looks, or always-valid confidence sequences that are correct under continuous monitoring. Compounding it, the effect size reported conditional on having crossed the threshold is biased away from zero, and the bias is larger the lower the power was, so an underpowered test that 'won' typically overstates the lift it found.

02

Reading a pooled rate that moved because the mix moved, not because any behaviour changed

A pooled conversion rate is a weighted average, and a shift in the weights can move it in the opposite direction to every one of its parts. A paid campaign that brings low-converting traffic drops overall signup conversion even if desktop, mobile web and app conversion each rose that week, which is Simpson's paradox and it is the single most common cause of an inexplicable dashboard move. The discipline is to decompose before explaining: recompute the rate holding last period's segment weights fixed, and compare that counterfactual to the actual, so the mix effect and the rate effect are separated numerically rather than argued about. Segment on the dimensions that actually reweight, which in this domain are almost always device_type, referrer_channel, country and new versus returning.

03

Reaching for a model before the target metric exists

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

04

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.

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

In Python, how would you implement a function to calculate the correla…

medium
statistics and probability

In Python, how would you implement a function to calculate the correlation between two variables?

Approach
  1. Write down the assumption the method needs before you use the method.
  2. Say what the estimate is of, and over what population it generalises.
  3. Sanity-check the answer against a simple bound or a simulated case.
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?

What are the assumptions of linear regression?

medium
machine learning and modelling

What are the assumptions of linear regression?

Approach
  1. Pick an evaluation metric that matches the cost of each error type, not a default.
  2. Set a baseline first, so any model has something honest to beat.
  3. Say how the offline result would be validated online before it is trusted.
Follow-up
  • Where could label leakage enter this setup?
  • What would you monitor after launch to know the model is still valid?

Describe how you would optimize a computationally intensive algorithm.

medium
machine learning and modelling

Describe how you would optimize a computationally intensive algorithm.

Approach
  1. Say how the offline result would be validated online before it is trusted.
  2. Set a baseline first, so any model has something honest to beat.
  3. Check what information would not exist at prediction time, and exclude it.
Follow-up
  • What would you monitor after launch to know the model is still valid?
  • How would you choose the decision threshold, and who owns that choice?

Split a pooled conversion drop into rate and mix

mediumWorked solution
mix shiftdecompositionpandas

You have weekly visit-to-signup counts by segment: a DataFrame with week, device_type, referrer_channel, visitors and signups. The pooled rate fell 0.84 percentage points between two consecutive weeks while several individual segments rose. Write a function that, for a caller-supplied list of segment columns, splits the pooled change into a rate effect, a mix effect and an interaction term that sum exactly to the observed change. Return those three scalars plus a per-segment contribution table sorted by absolute contribution, so the largest single driver can be named.

Approach
  1. State the algebra before coding: the pooled rate is r = sum over segments of w_s * r_s, with w_s the segment's share of the denominator. Then r1 - r0 decomposes exactly into sum(w_s0 * (r_s1 - r_s0)) for rate, sum((w_s1 - w_s0) * r_s0) for mix, and sum((w_s1 - w_s0) * (r_s1 - r_s0)) for interaction. The identity is per-segment, so it holds for any numbers you put in the four slots.
  2. Pivot both weeks onto a common segment index with an outer join so a segment that appeared or vanished is kept rather than dropped, then decide what rate to give a segment with no visitors in one of the weeks, and document the choice. The identity stays exact either way because the missing week's weight is 0, but the attribution does not. Filling the missing rate with 0 sends an appearing segment's entire w_s1 * r_s1 into the interaction term, since w_s0 = 0 makes both the rate term and the mix term (w_s1 - w_s0) * r_s0 identically zero; a vanishing segment then splits as -w_s0 * r_s0 in rate, -w_s0 * r_s0 in mix and +w_s0 * r_s0 in interaction.
  3. The convention used below instead imputes the missing week's rate as that week's pooled rate. A vanishing segment then lands wholly in mix at -w_s0 * r_s0, with rate and interaction cancelling; an appearing segment puts w_s1 * r_pooled0 in mix (volume arriving at the average rate) and only w_s1 * (r_s1 - r_pooled0) in interaction (its rate differing from that average). Impute by which week the segment is missing from, never by argument order, or the swap identities below stop holding.
  4. Guard the division where visitors is 0 so no NaN enters the vectors, because a single NaN poisons every sum. A segment with zero visitors in both weeks contributes exactly 0 and can be dropped; a segment missing from only one week does not contribute 0, and where its contribution lands is settled by the convention above, not by the guard.
  5. Compute the three components as vectors over segments, then sum. Keep the vectors, because the per-segment contribution table is what turns the decomposition into an explanation.
  6. Assert that the three components sum to the observed pooled change within floating-point tolerance. This identity is exact, so a mismatch means an implementation bug, not a modelling judgement.
Worked solution 25 min
  1. Aggregate to one row per (week, segment tuple) with summed visitors and signups, then split into w0 and w1 frames and align with an outer join, filling missing visitors and signups with 0.
  2. Compute w_s = visitors / visitors.sum() within each week, and r_s = signups / visitors only where visitors > 0. Where a week's visitors are 0, set that week's r_s to that week's pooled rate, the stated convention; never leave it NaN.
  3. rate_effect = (w0 * (r1 - r0)).sum(); mix_effect = ((w1 - w0) * r0).sum(); interaction = ((w1 - w0) * (r1 - r0)).sum().
  4. contribution = w0*(r1-r0) + (w1-w0)r0 + (w1-w0)(r1-r0) per segment, which reduces to w1r1 - w0r0; sort by abs and return the head.
  5. assert abs(rate + mix + interaction - (pooled1 - pooled0)) < 1e-12.
EXPECTED RESULTThree scalars summing exactly to the observed pooled change, plus a segment table whose contribution column also sums to that change. In the stated case rate and mix carry opposite signs: several segments converted better while volume moved toward a weaker-converting one, which is what makes the pooled move look inexplicable until it is split.
Follow-up
  • The mix effect accounts for 0.71 of the 0.84 point drop, driven by paid_social volume. What is your recommendation, and what would change it?
  • Why is a two-way split into a counterfactual rate and a residual also exact, and when would you prefer it to the three-way version?
  • Segmenting on device and channel leaves a large interaction term. What does that tell you about the choice of segments?

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.

Interviewers here are not checking whether you can describe a project. They want the decision you made, why you made it under the information you had, and what changed afterwards that someone else could measure. A story that ends at 'I built a model' has no ending. Say what the model caused, or what you stopped doing because of it.

Tell us about a time you had a conflict with a teammate and how you re…

medium
behavioural and stakeholder questions

Tell us about a time you had a conflict with a teammate and how you resolved it.

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
  • How did you know the outcome was caused by your change?
  • What would you do differently if you ran that project again?

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?

Walk through an analysis you got wrong and what changed

easy
postmortemdata qualityself-assessment

Describe an analysis of yours that turned out to be wrong after somebody had already acted on it. You have four minutes. The account must name the defect mechanically, the join, the filter, the window or the identity key, rather than describing it as a communication problem. It must also say who did what because of the wrong number, how the error surfaced, how long it stood, and what control you put in place so that class of error cannot reach a decision again. Do not pick an error nobody acted on.

Approach
  1. Recognise what is being probed: whether you can be specific about your own failure without minimising it or performing contrition. The discriminator is whether the defect has a mechanism the listener could reproduce in their own warehouse.
  2. Choose the case by blast radius rather than by comfort. An error nobody acted on tests nothing, and picking one signals that you are managing the interview instead of answering it.
  3. Structure the account in six beats: the number, the decision it drove, the defect, the detection, the correction, the control. Keep the defect to one reproducible sentence, for example an inner join to fct_subscription_period that dropped accounts with no subscription row and so computed retention over payers only.
  4. State the direction of the bias, not only its existence. A filter or join that removes rows usually moves a metric predictably, and knowing which way shows you diagnosed the mechanism rather than patched the symptom.
  5. Be exact about detection and elapsed time. 'A colleague noticed' and 'the row-count assertion failed before publication' are different answers about the same organisation, and the second one is the one your control is supposed to produce next time.
  6. End on the control, its cost, whether it has fired since, and one thing it does not cover.
Follow-up
  • What did the control cost, and has it fired since? If it never has, how do you know it works?
  • How long did the wrong number stand before anyone questioned it, and what does that say about the review path it went through?
  • What is the equivalent mistake you are most likely to make in this role, given the tables you would be working in?
  • 01

    Tell us about a time you had a conflict with a teammate and how you resolved it.

  • 02

    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.

  • 03

    Describe an analysis of yours that turned out to be wrong after somebody had already acted on it. You have four minutes. The account must name the defect mechanically, the join, the filter, the window or the identity key, rather than describing it as a communication problem. It must also say who did what because of the wrong number, how the error surfaced, how long it stood, and what control you put in place so that class of error cannot reach a decision again. Do not pick an error nobody acted on.

PracHub interview preparation framework ↗
Is this an official PrizePicks interview guide?

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

PracHub interview research ↗
How challenging are the interviews for this position?

The interviews are moderate in difficulty, focusing on both technical skills and cultural fit. With thorough preparation, you should feel confident in demonstrating your capabilities.

PracHub interview research ↗
What distinguishes successful candidates at PrizePicks?

Successful candidates typically demonstrate a strong technical foundation, a passion for sports, and the ability to communicate insights effectively within a collaborative environment.

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

The process generally takes 2-4 weeks, depending on scheduling and the number of interview rounds.

PracHub interview research ↗
Are remote work options available?

PrizePicks offers flexible working arrangements, including remote and hybrid options, depending on team needs.

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.