Optiver's Data Scientist interview, as candidates describe it, starts with a timed online assessment, then moves to HR and technical conversations and a take-home or data challenge. Probability, mental math and defending modeling choices come up repeatedly.
$115K
Avg. Base Comp
$190K
Avg. Total Comp
5 rounds
Typical Rounds
1-2 months
Process Length
Candidates describing Optiver's Data Scientist process mention a timed online assessment first. One recalls a three-hour HackerRank covering coding, probability, statistics and numerical sequences, including an order-book trade analyzer. Another describes a mix of games, probability and IQ-style puzzles, and a third describes two to three hours of probability, math games and coding. A graduate-level candidate reports a recursion-heavy random-walk card game and a streaming trade-analysis problem, so speed under time pressure matters.
After the assessment, candidates report an HR conversation with behavioral questions, such as past mistakes, motivation and teamwork. Technical rounds are described as Fermi estimation, game theory, coin-toss expected payoff and a table-on-a-circle probability puzzle. One candidate also reports live trading games that grew more complex between rounds.
Several candidates describe a take-home or data challenge. One was a 48-hour network routing analysis that was not on financial data. Another candidate was pushed to justify every modeling choice: ridge versus lasso, hyperparameters, feature correlations and supervised versus unsupervised assumptions. Practice explaining why you chose an approach, not only what you built.
The five reports here all ended in no offer, and they differ in round count and format. Treat the stage order as a likely shape, not a fixed sequence.
Synthesized from 5 candidate 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 Optiver process.
The process started with an online assessment on HackerRank that felt like a marathon—three hours covering coding, probability, statistics, and numerical sequences all in one go. I remember one question asked me to code up a trade analyzer from an order book, which combined algorithmic thinking with domain knowledge. After that initial filter, I moved to an HR phone screen that was pretty straightforward, mostly behavioral questions about mistakes I'd made and motivations for the role.
The technical interview came next, and this is where things got intense. I was hit with Fermi problems and game theory questions right away, which tested intuition more than pure coding. Then the conversation shifted to probability and statistics—they asked about a table with three legs placed randomly on a circle and whether it would fall, and probed deeper into Markov chains and coin toss scenarios. When we got into the data challenge portion, I had to present my results, and that's where I stumbled. The interviewer went deep on feature correlations, asking why I'd chosen one model over another and justifying specific hyperparameters. They pushed hard on regularization choices, specifically ridge versus lasso, and kept asking me to defend my assumptions about supervised versus unsupervised approaches.
The whole thing stretched over about a month with a fairly casual vibe, but the time pressure was real—especially in the online assessment and during that technical round. I didn't make it past the data challenge presentation, which honestly surprised me since I'd felt okay about the earlier rounds. Looking back, I think I underestimated how thoroughly they'd drill into the why behind every modeling choice.
Prep tip from this candidate
Expect intense questioning on model selection rationale and regularization trade-offs (ridge vs. lasso, supervised vs. unsupervised). Practice Fermi problems and probability questions like the three-legged table scenario, and be prepared to present and defend a data challenge result under scrutiny—that presentation round is critical and high-pressure.
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 Optiver
Write a function compute_deviation that returns the standard deviation of each list in a list of dictionaries
| Question | |
|---|---|
| Experiment Validity | |
| 500 Cards | |
| Session Difference | |
| Maximum Profit | |
| Sum to N | |
| Network Experiment Design | |
| Profit-Maximizing Dice Game | |
| Scalped Ticket | |
| Flipping 576 Times | |
| Stranded Miner | |
| Hurdles In Data Projects | |
| Priority Queue Using Linked List | |
| 85% vs 82% | |
| Bootstrapping Confidence Intervals | |
| New UI Effect | |
| Digital Marketing Metrics | |
| Interquartile Distance | |
| Slow SQL Query | |
| Addressing Data Quality Issues | |
| Index Fund Return | |
| Minimum Days for Scheduling All Meetings | |
| Client Solution Pushback | |
| Ranking Metrics | |
| Identifying Good Investors | |
| Processing Large CSV | |
| LRU Cache 1 | |
| Six Face Die | |
| 2nd Highest Salary | |
| Empty Neighborhoods |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report a timed online assessment as the first filter. Descriptions include a three-hour HackerRank with coding, probability and statistics, and a multi-part test with games and puzzles. One graduate candidate reports a recursion-based card game and a trade-analysis stream. Expect heavy time pressure.
Candidates report an HR conversation that is mostly behavioral. Topics include mistakes you have made, why Optiver and the role, and teamwork. One candidate read the teamwork focus as a sign that collaboration matters. It is typically less technical than the other stages.
Candidates describe technical rounds built around Fermi estimation, game theory, coin-toss payoffs, Markov chains and a probability puzzle about a table with three legs. One candidate reports live trading games that grew more complex. Interviewers may probe your reasoning more than your coding.
Several candidates report a take-home assignment. One was a 48-hour network routing and order-flow analysis, not a financial dataset. Another candidate reports presenting data challenge results. Be ready to explain feature choices, model selection, hyperparameters and assumptions, since interviewers may press on the reasoning.
Candidates who reach this stage describe a mix of formats. One reports final HR and technical discussions of the assignment, with ML techniques and their assumptions. Another recalls a roughly five-hour onsite with multiple interviewers. Formats vary by candidate, so confirm the structure with your recruiter.
