Optiver Data Scientist Interview Guide (2025) – Process, Questions, and Salary Insights

Optiver data scientist roles sit at the intersection of quantitative research and real-time trading, where every insight can directly influence market outcomes. This guide will walk you through the interview process, highlight the question types you’ll encounter, and share best practices to demonstrate your statistical rigor and low-latency execution skills under pressure. You’ll learn how to prepare for each stage—from initial screens to on-site deep dives—and how to align your experience with Optiver’s fast-paced, results-driven culture.

Data Science at Optiver thrives on collaboration with traders and quants to develop predictive pricing models, run controlled market experiments, and automate risk controls in environments where milliseconds matter. With direct P&L impact and a culture that rewards ownership, the bar is set high—but so are the rewards in compensation and career growth. Ready to dive into the details?

Role Overview & Culture

An Optiver data scientist partners closely with quantitative researchers and trading teams to build end-to-end pipelines that ingest massive tick data, apply advanced statistical and machine-learning techniques, and deploy predictive models directly into production trading systems. You’ll design A/B-style market experiments to validate new strategies, implement automated risk controls to protect capital, and continuously refine models based on live feedback. Small, autonomous teams drive these projects from hypothesis through execution, embodying Optiver’s “own-it” ethos and bias for action. Transparency, rapid iteration, and a relentless focus on low-latency performance define the day-to-day, ensuring your analyses translate into tangible trading advantages.

Why This Role at Optiver?

Joining Optiver as a data scientist means seeing the immediate impact of your work on trading profitability and market efficiency. You’ll enjoy the freedom to experiment with novel approaches, access to cutting-edge tools like Kafka and Flink, and market-leading compensation that reflects the high stakes of your contributions. Optiver’s meritocratic environment accelerates career progression for those who deliver results—promotion cycles are swift, and successes are recognized quickly. With a flat organizational structure and a culture that prizes measurable outcomes, your innovative ideas won’t get lost in bureaucracy but will drive real business value. Next, we’ll break down the Optiver data scientist interview process so you can prepare confidently.

Optiver’s data scientist interview loop is designed to assess both your quantitative expertise and your ability to translate insights into real-time trading advantages. You’ll move from an initial recruiter screen through hands-on technical assessments, deep statistical discussions, and culture-fit conversations. Each stage evaluates a different facet of the role—coding proficiency, probabilistic reasoning, trading domain knowledge, and alignment with Optiver’s rapid-decision culture.

Application & Recruiter Screen

Your journey begins with a recruiter phone call focused on your résumé, academic background in statistics or machine learning, and motivation for joining a proprietary trading firm. Expect questions about past projects involving large-scale data or real-time systems, as well as discussion of your familiarity with Python and time-series analysis. This call also covers logistical details like location, work authorization, and compensation expectations. Clear communication and concise articulation of your analytical experience will help you advance to the next round.

Take-Home Case or Coding Screen

In this stage, you’ll be given a two- to three-hour take-home assignment—often delivered as a Jupyter notebook—to work with market-making data and build or evaluate predictive models. The exercise is meant to simulate real tasks an Optiver data scientist might face, such as crafting feature pipelines, handling streaming tick data, or implementing simple reinforcement-learning strategies. Interviewers will judge not only the correctness of your code but also its efficiency, readability, and robustness against data anomalies. You’ll submit your notebook for review before being invited to live interviews.

Technical Deep-Dive Interviews

During these live sessions, expect a blend of probability puzzles, Bayesian inference problems, and scenario-based questions grounded in trading. You might derive closed-form solutions for option-pricing adjustments or discuss how you’d detect regime shifts in market volatility using change-point detection. These rounds probe your mathematical rigor, your ability to reason under pressure, and your understanding of how statistical models drive trading decisions. Demonstrating both depth and clarity—walking through your thought process step by step—is key to impressing your interviewers.

Behavioural & Culture Interviews

Optiver values candidates who “own it” and move at the speed of markets. In these conversations, you’ll share stories illustrating how you’ve taken full responsibility for a project, navigated tight deadlines, or collaborated with non-technical stakeholders. Interviewers look for evidence of quick decision-making, clear communication under stress, and a bias for action. Be prepared to discuss times you discovered data issues in production, led root-cause analyses, and implemented safeguards that prevented future incidents.

Offer & Team Match

Once you’ve cleared the technical and cultural gauntlet, Optiver’s hiring committee convenes to review feedback and calibrate offers. If approved, you’ll receive a detailed compensation package and be matched with a trading desk or research pod based on mutual fit. This final stage is also your opportunity to ask questions about team dynamics, technology stacks, and roadmap priorities before accepting.

Behind the Scenes

Feedback is typically provided within 24 hours after each interview round, thanks to Optiver’s streamlined panel-scoring process. A small hiring committee vets all scores and can veto any candidate if concerns arise, ensuring only top performers progress. This rapid cycle reflects the firm’s commitment to decisiveness and high standards.

Differences by Level

Junior data scientists (L3–L4) focus on maintaining and scaling existing models, handling data-quality checks, and supporting senior colleagues on feature engineering. Senior hires (L5+) are expected to propose and architect entirely new alpha pipelines, lead end-to-end research projects, and mentor more junior team members. In both cases, demonstrating ownership and a track record of impactful delivery is essential.

The Optiver data scientist interview is structured to evaluate your technical rigor, experimental thinking, and cultural alignment through three core question categories. Each segment probes a different aspect of the role—from deep algorithmic skills to practical market-driven experiments to “own-it” mindsets essential in fast-paced trading environments.

Coding / Technical Questions

In this portion, you’ll tackle probability and statistical puzzles that mirror real trading uncertainties, such as deriving conditional expectations under transaction-cost constraints. Expect hands-on coding tasks in Python, focusing on time-series feature extraction and the efficient implementation of Monte Carlo or Bayesian inference routines under strict latency targets. Interviewers look for clear, optimized code as well as a solid grounding in numerical stability and algorithmic complexity.

1. How would you build a function that pairs every friendship “add” with its corresponding “remove” event so you can output the start- and end-timestamps for each friendship cycle?

   Read the two event streams as an unordered log of “added” and “removed” messages. Store the earliest unmatched “add” timestamp for every unordered pair of user-IDs in a hash-map (normalise the key so A-B equals B-A). When a “removed” record arrives, pop the matching start-time from the queue for that pair and emit a tuple (user_1, user_2, start_ts, end_ts). Because each event is touched once, the algorithm runs in O(N) time and holds only current friendships in memory.

2. How can you write a query that returns TRUE or FALSE for each user according to whether any two of their completed subscription periods overlap?

   Self-join the subscription table on user_id with an inequality that compares every interval to every other for that same user. An overlap exists when start_a < end_b and start_b < end_a. Group by user_id and aggregate with BOOL_OR() (or MAX(flag)) to surface a single Boolean per user without enumerating every conflicting pair.

3. Given Tinder swipe logs and AB-test assignment, how would you compute the mean number of right swipes for each variant among users who have reached 10, 50, and 100 total swipes?

   First summarise the swipes table per user into total_swipes and right_swipes. Bucket those users by the smallest threshold they’ve cleared (≥10, ≥50, ≥100). Join that summary to the variants table, then, within each (variant, bucket) group, calculate the average of right_swipes. Users below the 10-swipe threshold are excluded to keep comparisons fair.

4. How would you create a new “flight_routes” table that lists each origin-destination pair only once, regardless of flight direction?

   When inserting, reorder the two airport codes alphabetically so BOS-JFK and JFK-BOS collapse into the canonical key (airport_a, airport_b). Insert distinct keys into the destination table and enforce a composite primary key on those two columns to prevent duplicates going forward.

5. Using mutual friends (+3 points each) and shared page-likes (+2 points each), who should be recommended as John’s next friend, excluding anyone he has blocked or already befriended?

   Build two sub-scores per candidate: count mutual friends and multiply by 3; count common page likes and multiply by 2. Filter out users found in John’s blocked_users or friends tables, sum the scores, and order descending. The highest-scoring remaining user becomes the recommendation; tie-break with a stable field such as user_id.

6. How would you identify—for every advertiser that topped weekly revenue in 2021—their three best single-day revenues during those winning weeks?

   Step 1: aggregate 2021 revenue by (advertiser, week) and pick the max-revenue advertiser per week. Step 2: within those advertiser-week combinations, rank days by revenue with ROW_NUMBER() and keep rows where rank ≤ 3. This two-phase method avoids unnecessary ranking of advertisers who never led a week.

7. How can you sample a key from a weight-dictionary so the selection probability is proportional to each key’s weight?

   Pre-compute a cumulative weight array and total weight W. Draw a uniform random float in [0, W) and binary-search the cumulative array to locate the bucket it falls into; return the associated key. This guarantees correct probabilities and supports repeated draws in O(log n) time after the one-time preprocessing step.

8. How can you decide if two axis-aligned rectangles overlap—even if they just touch at an edge or corner—when their corner coordinates come in any order?

   Reduce each rectangle to four scalars: min_x, max_x, min_y, max_y. The pair overlaps unless one lies completely to the left, right, above, or below the other. Formally, they overlap when NOT (r1.max_x < r2.min_x OR r2.max_x < r1.min_x OR r1.max_y < r2.min_y OR r2.max_y < r1.min_y).

9. Given an array containing every integer from 0 to n except one, what O(n) / O(1) method finds the missing value?

   Compute the expected arithmetic sum n × (n + 1) / 2, subtract the actual sum of the array, and the difference is the missing number. One pass through the data and two integer accumulators suffice.

10. Two one-hour nightly jobs each start at a random minute between 7 pm and midnight; if they overlap, the company loses $1 000. What single number would you give the CEO for the expected annual cost?

    Each start time is uniform over a 5-hour window (300 minutes), so modelling the two independent start times forms a 300 × 300 square. The area where their one-hour windows intersect is 60 × 240 × 2 = 36 000; dividing by the total area (90 000) gives a 0.4 probability of collision—≈ 40 %. Multiplying 0.4 × $1 000 × 365 yields an expected cost of about $146 000 per year (a quick Monte-Carlo confirms the same ballpark).

11. Given parallel lists of user_id and tip_amount, how would you identify the user who has tipped the most in total?

    Traverse the lists once, adding each tip to a running total stored in a hash-map keyed by user_id. Keep track of the maximum total seen so far (and an optional tie-break rule). After one linear pass, the key with the highest aggregate value is returned as the biggest tipper.

Product / Experiment-Design Questions

Here, you’ll demonstrate how you’d design robust, data-driven tests and monitoring frameworks for production trading models. Scenarios include crafting an A/B test to compare quoting strategies in live markets and setting up real-time drift detection in order-book predictions. Strong candidates articulate measurable success metrics, discuss sample-size and power considerations, and propose guardrail metrics to safeguard P&L against adverse selection.

1. How would you analyze search-result logs to verify whether higher human-relevance ratings (1 – 5) actually correlate with better click-through performance?

   Aggregate the search_results table by rating, computing total impressions and clicks (SUM(has_clicked)). Then calculate CTR = clicks / impressions for each rating bucket and add confidence intervals or a two-proportion z-test to check statistical significance between adjacent ratings. A visualization of CTR versus rating quickly reveals monotonicity; a logistic-regression of click ~ rating (plus position and query fixed effects) controls for confounds such as rank bias and query difficulty. Discuss how to down-weight sparse buckets and why you would segment by device or locale before shipping relevance-model changes.

2. What are Z-tests and t-tests, when are they appropriate, and how do you choose between them?

   Both tests compare sample means against a hypothesised population mean (one-sample) or against each other (two-sample). A Z-test assumes the population standard deviation is known or the sample size is large enough (≈ n ≥ 30) for the central-limit theorem to make the sample SD a good proxy. A t-test uses the Student-t distribution to account for extra uncertainty when the variance is estimated from the data, especially with small n. In practice: large-scale A/B experiments with millions of users → Z-test; small lab studies or sliced cohorts → t-test. Always verify normality or rely on CLT before applying either parametric test.

3. If a company can only pre-launch a new show to 10 000 users, how would you pick that audience and measure success?

   Start by defining the evaluation metric (e.g., completion rate, incremental watch time). Use stratified random sampling so the 10 000 customers mirror the full catalog of regions, devices, viewing frequencies, and content preferences; this preserves external validity. Hold out an equal-sized control group for comparison, instrument the UI to capture engagement signals, and monitor north-star plus guardrail metrics (churn, bandwidth cost). After the two-week pilot, run diff-in-diff analyses and Bayesian credible intervals to decide whether to scale or tweak marketing. Flag that selection bias will arise if you cherry-pick “power users” rather than a representative slice.

4. A redesigned onboarding email lifted conversion from 40 → 43 %, but rates were 45 % months earlier. How would you test if the uplift is causal?

   First, build an interrupted time-series that models conversion as a function of time plus a step change at launch; include seasonality covariates to separate secular trends. If historical drift explains the bounce-back, the intervention term will be insignificant. Better, retroactively assign users to “treated” (new journey) and “untreated” (hold-back or late adopters) cohorts and apply a difference-in-differences estimator on matched sign-up dates. Propensity-score matching on acquisition source controls for mix shifts, and robustness checks (placebo dates, falsification outcomes) shore up inference.

5. A landing-page A/B test shows p = 0.04. What validity checks would you run before trusting the win?

   Confirm sample-ratio-mismatch (SRM) isn’t present by χ²-testing bucket counts; large SRM voids the p-value. Inspect pre-experiment metrics to ensure covariate balance, audit event logging for missing data, and verify that the test ran long enough to cover at least one full business cycle. Apply sequential-test correction if the PM peeked early and adjust for multiple metrics with Holm-Bonferroni or FDR. Finally, compute practical significance (lift × user base) and confidence interval width—statistical significance without business impact is not a win.

6. When you’re running hundreds of simultaneous t-tests, what adjustments and monitoring strategies prevent a flood of false positives?

   Control the family-wise error rate with Bonferroni/Holm or, more power-efficiently, the false-discovery rate via Benjamini–Hochberg. Pre-register hypotheses and group related tests into logical families. Use hierarchical models or Bayesian shrinkage to borrow strength across variants and reduce variance. Automate dashboards that flag the proportion of significant results versus expectations under α to detect p-hacking drift.

7. How would you verify that A/B bucket assignment is truly random and unbiased?

   Run a χ² or KS test on key pre-experiment features (traffic source, device, prior activity) comparing variant distributions; any systemic difference indicates leakage in hashing or targeting. Plot sequential user-ID assignment over time to detect periodicity or clustering. For online bucketization based on user-ID hash, confirm the hash seed and modulus are stable in code reviews and logging. An SRM dashboard that triggers when observed allocation deviates from expected by > 3 σ provides ongoing guardrails.

8. How would you design an experiment to test both color (red → blue) and placement (top → bottom) changes to a sign-up button while measuring funnel impact?

   Use a 2 × 2 factorial design producing four variants: {red-top, red-bottom, blue-top, blue-bottom}. This lets you estimate main effects and interaction with half the traffic of two sequential A/B tests. Define primary KPI as click-through to the next funnel step, with sign-ups and retention as secondaries. Randomly assign users at the session or cookie level, ensure equal power across cells, and run for a full business cycle. Analyse via two-way ANOVA or logistic regression including interaction terms, then pick the variant with highest lift if no interaction, or the winning combination if interaction is significant.

9. Given ab_tests and subscriptions, how would you query variant-specific conversion rates where “conversion” rules differ for control versus trial users?

   Build two CTEs keyed by user_id: (1) control—flag converted = TRUE if any downstream subscription row exists; (2) trial—flag only if the subscription either has cancel_date IS NULL or DATEDIFF(cancel_date, start_date) ≥ 7. Union them, group by variant, compute 100 * AVG(converted) and round to two decimals. This handles heterogeneous success criteria while returning a single tidy table of conversion percentages.

10. Robinhood sent 1 M “market opening” push notifications. What steps would you take to analyse lift and present final results?

    First check SRM, then compute primary outcome (e.g., trades per user in first hour) with CUPED adjustment to cut variance. Segment by activity decile and device to surface heterogeneous effects. Visualise lift over time—does excitement decay? Report average treatment effect, 95 % CI, and incremental revenue; consider notification fatigue metrics (disable rates) as guardrails before scaling to the full base.

11. Design and analyse an A/B test on two payment pages to maximise completed check-outs—what factors must you control?

    Define conversion as successful payment within same session; randomise at the user level; guard against caching effects by serving static assets via versioned URLs. Monitor drop-off at each funnel step to diagnose UX issues. Pre-compute required sample size for an expected 1 % absolute lift at 80 % power, apply sequential testing if business demands early readouts, and adjust for multiple geos if stratified. Post-experiment, run χ² or logistic regression controlling for cart value and device to validate robustness.

12. How would you allocate marketing budget across YouTube, Google Search, Facebook Ads, and Direct-Mail while testing efficiency?

    Use a multi-cell geo-lift or market-split experiment: randomly assign DMA regions to one of the four channels or a hold-out, keeping budget proportionate to historical spend. Measure incremental conversions per dollar via pre-post comparisons in each region; CUPED can reduce variance by using prior-period KPIs as covariates. Run for at least two purchase cycles, then build a response curve model (e.g., log-log or Diminishing-Returns) to determine marginal CPA per channel. Re-optimise the budget by equalising marginal ROAS or via constrained optimisation to stay within total spend.

Behavioral & Culture-Fit Questions

Optiver values engineers who “own-it” end to end. In this segment, you’ll share stories of taking responsibility for a misfiring model deployment, balancing the need for rapid iteration with system reliability, and collaborating effectively with quantitative traders under tight deadlines. Demonstrating clear communication, decisive action, and lessons learned from past failures signals your readiness to thrive in Optiver’s high-velocity, impact-focused culture.

1. Can you walk me through a recent data-science project you led, and explain the toughest hurdles you encountered and how you cleared them?

   Optiver analysts frequently back-test strategies on terabytes of tick data and must deliver results on tight iteration loops. Good answers describe a concrete project—e.g., latency-sensitive feature engineering or a mislabeled options data set—and outline how you diagnosed the issue, quantified risk to P&L, and iterated until the model was production-ready. Highlight tooling (Spark, GPUs, custom C++), the trade-offs you made between speed and rigour, and what you’d improve next time.

2. What practical tactics do you use to translate complex market-microstructure insights into dashboards or narratives that non-technical traders can act on immediately?

   At Optiver, quants often brief traders who have seconds to decide on positions. Explain how you strip noise by surfacing only statistically significant signals, use real-time Grafana or KDB dashboards, and employ intuitive visuals such as heat-maps of bid–ask depth. Mention storytelling—linking a metric directly to trading edge—and how you measure comprehension and adoption.

3. If we asked your current manager for three strengths and three areas to improve, what would they say—and how have you acted on that feedback?

   Optiver wants candid self-assessment and a growth mindset. Frame strengths that map to the role (e.g., “deep options-pricing intuition,” “coding speed under pressure”) with supporting anecdotes. Choose improvement areas you’ve already addressed—perhaps writing more robust unit tests or delegating better—then describe the concrete steps you took (pair coding, code-review checklists) and the measurable outcome.

4. Describe a time you struggled to get a trading desk or risk team on board with your analytical recommendation. How did you realign and drive a decision?

   Successful stories show you listened to desk objections, re-ran sensitivity analyses, and presented profit-and-loss scenarios that resonated with their risk appetite. Call out empathy, data visualisation tweaks, and iterative feedback loops that turned sceptics into champions without delaying the market opportunity.

5. Why are you interested in joining Optiver, and what unique value would you add to our research culture?

   Tailor your answer to Optiver’s philosophy of disciplined market-making and technology-driven edge. Reference the firm’s open-source contributions, its low-latency stack, or its collaborative researcher-trader model, and connect that to your expertise in, say, reinforcement-learning agents for order-book optimisation. End by tying your career goals to Optiver’s mission of improving market efficiency.

6. How do you juggle multiple ad-hoc desk requests, long-term research agendas, and production monitoring without missing critical deadlines?

   Outline a priority matrix anchored on expected P&L impact and risk; mention daily stand-ups with traders, Kanban boards, and automated regression tests that free you from firefighting. Stress communication—flagging trade-offs early—and the habit of time-boxing exploratory work so urgent signal checks don’t derail strategic initiatives.

7. Tell us about a time your model’s live performance deviated from back-test expectations. How did you detect the drift and what mitigation steps did you take?

   Interviewers want evidence you can own models end-to-end. Discuss monitoring pipelines (population-stability index, feature-distribution alerts), quick root-cause analysis, and whether you rolled back, recalibrated, or hedged exposure while fixing the issue. Emphasise post-mortem culture and preventive safeguards you instituted afterwards.

8. When market conditions change abruptly—say, during a macro shock—how would you adapt your research roadmap to keep Optiver’s edge?

   Good responses cover fast hypothesis generation, rapid A/B deployments in simulation, and coordination with risk managers to widen guardrails temporarily. Describe the balance between opportunistic short-term tweaks (e.g., volatility-regime features) and longer-term model refactors, showing strategic agility without compromising statistical discipline.

Getting ready for the Optiver data scientist interview requires both domain-specific practice and cultural alignment. You’ll need to balance deep statistical reasoning with rapid, production-grade coding, all while demonstrating the “own-it” mentality that underpins Optiver’s high-velocity trading culture. Here are some targeted ways to sharpen your preparation:

Study the Role & Culture

Revisit Optiver’s core values around ownership, speed, and collaboration. Understand how research insights translate directly into P&L impact, and be prepared to discuss times you’ve moved quickly under uncertainty without compromising on rigor or integrity.

Practice Common Question Types

Break your prep into three buckets: ~40 % probability & statistical puzzles, 30 % Python/pandas coding exercises, and 30 % behavioral scenarios. Use past interview questions or platforms like Interview Query to simulate each segment under realistic time constraints.

Think Out Loud & Ask Clarifying Questions

Interviewers look for candidates who articulate their assumptions, trade-offs, and decision rationale explicitly. When presented with a problem, outline your plan step by step and check in with your interviewer before diving into implementation.

Brute Force, Then Optimize

Start by coding a straightforward solution—even if it’s not optimal—then iteratively refine it for performance and latency. Highlight how you balance correctness with the low-latency demands of market-making systems.

Mock Interviews & Feedback

Partner with data scientists who have experience at trading firms or leverage Interview Query’s mock-interview service to run through full loops. Solicit candid feedback on your statistical reasoning, code clarity, and cultural fit.

Mastering trading-centric statistics, rapid algorithmic coding, and clear trade-off communication is the fastest route to success in the Optiver data scientist interview. Ready to level up? Book a mock interview, explore our Data Science learning path, or dive into the broader Optiver interview questions pillar for cross-role insights.

See how Muhammad Imran Haider navigated his process to land a role.