DraftKings Data Analyst candidates report a recruiter screen, phone expected-value cases, and a Zoom case with an analytics manager. Here is what they were asked and how to prepare for the case-heavy format.
$92K
Avg. Base Comp
$110K
Avg. Total Comp
3-5 rounds
Typical Rounds
Not reported
Process Length
Candidates describing DraftKings analytics interviews put case studies at the center of the process. Two Data Analyst candidates and one Business Data Analyst candidate describe an HR screen followed by a phone case built on expected value and probability. The prompts involved ticket purchases, including a scalped ticket with a chance of being fake or a bowling-game ticket scenario. One candidate also had a raffle layered on top and had to say which option had the highest EV.
The next stage is reported as a business case. One Data Analyst candidate faced a Zoom case with an analytics manager and did not get past it. The Business Data Analyst candidate got a train-dissatisfaction scenario. They listed hypotheses for the problem and sketched the graphs that would confirm or rule out each one. A Senior Analyst candidate describes similar train and ticket cases, so chart selection and talking through assumptions are worth practicing.
Results in these reports were mostly rejections. One candidate reached a final case that was cancelled, and the Senior Analyst candidate was told a final sportsbook and casino case went badly. That candidate said the missed points were business reasons for falling margin, and that sportsbook bets must be balanced. Those reports come from adjacent senior roles, so treat them as optional stretch preparation.
Practice speaking your structure aloud, state assumptions before calculating, and tie every number back to a business decision.
Synthesized from 6 candidate reports by our editorial team.
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Real interview reports from people who went through the Draftkings process.
I started with an HR screen that came inbound from the recruiter, and that part was pretty straightforward. After that, the process moved into a phone interview that leaned technical, but not in a super hard way. The main thing I remember is that they asked EV-style questions and then shifted into a case study format. The case itself was on Zoom with a manager from analytics, and that was where the interview got more interesting. It felt less like a pure coding screen and more like they wanted to see how I thought through a business problem and whether I could structure an answer clearly under pressure.
The case questions were the part that stood out most. One prompt was framed like a train question from other interviews, where you have to find the time sink, and another question was about probability, like estimating the chances of getting tickets for a bowling game on the ground or online. That gave me the sense they were testing both analytical reasoning and comfort with ambiguous, estimation-style problems. I didn’t make it past the first case, even though there was apparently another round after that, which I never reached. Overall the process felt manageable up front and then more selective once the case study started. If I had to do it again, I’d spend more time practicing how to talk through estimation and case prompts out loud, because that was clearly the filter.
Prep tip from this candidate
Practice estimation and probability-style case prompts, especially “find the time sink” train-type questions and business scenarios where you have to reason through assumptions out loud. Be ready to explain your approach clearly in a Zoom case with an analytics manager, since that seemed to be the main cutoff.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Draftkings
How much do you expect to pay and how much money should you set aside for the game
| Question | |
|---|---|
| Rider Discount | |
| Why Do You Want to Work With Us | |
| 2nd Highest Salary | |
| Compute Deviation | |
| Button AB Test | |
| P-value to a Layman | |
| Google Maps Improvement | |
| Group Success | |
| Significance Time Series | |
| Hurdles In Data Projects | |
| Centralized Event Ingestion | |
| Subscription Retention | |
| Comparing Search Engines | |
| Time on FB Distribution | |
| WAU vs Open Rates | |
| Marketing Channel Metrics | |
| Valid Anagram | |
| Find Duplicate Numbers in a List | |
| Spam Classifier | |
| Compute Variance | |
| Customer Success vs. Free Trial | |
| Distribution of 2X - Y | |
| Track Your Most Valuable Gamers | |
| Matrix Rotation | |
| KNN From Scratch | |
| Concurrent LLM Serving | |
| Interquartile Distance | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| User Event Data Pipeline |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter or HR call that was straightforward. One says it covered their background and why they wanted to work at DraftKings. Prepare a concise story and a clear reason for choosing the company.
Candidates report a phone interview built on expected-value questions, such as buying a possibly fake scalped ticket or comparing options with a raffle. Data Analyst candidates describe the math as manageable, and one found the estimation-style prompts ambiguous. Practice structuring EV comparisons aloud.
One Data Analyst candidate reports a Zoom case with an analytics manager that served as the main cutoff. A Business Data Analyst candidate reports a train-dissatisfaction case, where they listed hypotheses and the graphs that would support each. Expect to reason out loud.
A Business Data Analyst candidate reports a standard 30-minute video behavioral round. Another Data Analyst candidate says a final case was cancelled before it happened. Later stages appear to vary by candidate, so confirm each round with the recruiter.
