Draftkings Quantitative Analyst interview typically runs 3 rounds: recruiter call, phone screen, case study/technical round. Timeline is about 2-3 weeks, and the process is casual and conversational.
$77K
Avg. Base Comp
$110K
Avg. Total Comp
4-5
Typical Rounds
2-4 weeks
Process Length
We've seen DraftKings lean hard into expected value and practical judgment over abstract theory. In the candidate experience we reviewed, the problems were framed around everyday decisions — grocery stores, coupons, travel time, and threshold-based discounts — but the real test was whether the candidate could turn a messy scenario into a clean decision. That tells us the bar here is less about finding a perfect formula and more about showing disciplined reasoning when the inputs are uncertain or incomplete.
A recurring theme is that interviewers want to hear your assumptions out loud and see how you handle tradeoffs. Multiple candidates reported a casual, conversational tone, with interviewers focusing on clarity of thought rather than trying to spring traps. We also saw references to adjacent quantitative prompts like a grocery-store statistics exercise, an optimization-style subway question, and A/B testing with more expected value work, which suggests DraftKings values people who can move comfortably between probability, experimentation, and operational decision-making. The non-obvious make-or-break factor here is not speed — it’s whether your logic stays coherent when the scenario adds one more layer of uncertainty.
Synthetized from 1 candidates reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Draftkings process.
The part that stood out most was how much the interview leaned on expected value and practical judgment rather than anything overly technical. After a recruiter call and phone screen, I had a case study/technical round that was pretty straightforward in format but still required careful thinking. The interviewer gave me two problems centered on expected value, including a grocery-store scenario where I had to compare buying the same set of groceries at different stores while factoring in coupon rules, the chance the coupon would work, and even travel time. Another version of the question framed it as choosing between stores with different prices and a coupon that only worked if the purchase was over a threshold, with a probability attached to whether it applied. I also heard that the process included a statistics exercise about a grocery store, then a separate optimization-style question about a subway station, and later an A/B testing round with more expected value work, so the overall theme was definitely quantitative reasoning applied to real-world decisions.
The vibe was casual and conversational rather than intimidating. In the round I did, the interviewer seemed mainly interested in whether I could think clearly, explain my assumptions, and show solid problem-solving skills. There were also a couple of team leads in a virtual interview for the risk/fraud side who asked about my background and skills relevant to the role, and they were very open about their own backgrounds too. I didn’t feel like they were trying to trap me with trick math; it was more about how I approached uncertainty and tradeoffs. I ended up not getting the role because they filled it, though I was told I was still being considered for other positions. My main takeaway is to be ready for expected value questions that sound simple on the surface but require you to state assumptions cleanly and walk through the decision logically.
Prep tip from this candidate
Practice expected value cases that include coupons, probability of the coupon working, and travel time or other hidden costs. Also be ready to explain your assumptions out loud, since the interview seemed to care as much about reasoning and critical thinking as the final answer.
Share your own interview experience to unlock all reports, or subscribe for full access.
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 | |
|---|---|
| Processing Large CSV | |
| Why Do You Want to Work With Us | |
| 2nd Highest Salary | |
| Google Maps Improvement | |
| Compute Deviation | |
| Compute Variance | |
| Hurdles In Data Projects | |
| P-value to a Layman | |
| Integer to Roman | |
| Significance Time Series | |
| Distribution of 2X - Y | |
| Nearest Common Ancestor | |
| Track Your Most Valuable Gamers | |
| Subscription Retention | |
| Time on FB Distribution | |
| Find Duplicate Numbers in a List | |
| Valid Anagram | |
| Tower of Hanoi | |
| Simulating Coin Tosses | |
| Spam Classifier | |
| Overfit Avoidance | |
| Loan Model | |
| Customer Success vs. Free Trial | |
| Matrix Rotation | |
| KNN From Scratch | |
| Bootstrapping Confidence Intervals | |
| Interquartile Distance | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Video Game Respawn Model |
Synthesized from candidate reports. Individual experiences may vary.
An initial conversation with recruiting to discuss your background, interest in the Quantitative Analyst role, and overall fit. Based on the experience shared, this appears to be a standard first touchpoint before moving into the technical screen.
A screening interview focused on quantitative reasoning and practical judgment. Candidates should expect expected value-style questions and be prepared to explain assumptions clearly rather than rely on heavy technical math.
A structured problem-solving round centered on real-world decision making. Examples included grocery-store expected value scenarios involving coupons, probability of coupon success, travel time, and comparing options with different thresholds and prices.
Some candidates reported follow-up exercises covering statistics, optimization, and A/B testing. These rounds continued the same theme of applying quantitative reasoning to practical business problems, such as a grocery store statistics exercise or an optimization question about a subway station.
Later-stage virtual interviews with team leads, including risk/fraud stakeholders, focused on your background, relevant skills, and how you think through uncertainty and tradeoffs. The tone was described as casual and conversational, with an emphasis on communication and fit.