A Four-Week Quant Interview Study Plan

A useful quant interview plan starts with your target role and current gaps. Four weeks is a planning window, not a promise that every candidate can become interview-ready in a month. If you are still learning probability or programming fundamentals, give yourself more time.

Start with the free diagnostic. For each problem, record your answer, reasoning, time, and whether you used a hint. Getting a familiar answer right from memory is different from explaining a new variant.

Choose a role before choosing a syllabus

Trading: emphasize arithmetic, probability, expected value, decisions under uncertainty, and clear explanations. Add coding if your target role calls for it.

Quantitative research: emphasize statistics, experiment design, coding, time-series validation, and explaining a research project. Probability still matters, but research interviews can go well beyond puzzles.

Quant development: emphasize algorithms, data structures, testing, and the systems relevant to the job description. A low-latency C++ role and a Python research-engineering role need different preparation.

The employer's role description and invitation should determine the balance. For an example of research requirements, Jane Street's research role describes experiment design, model building, Python, and collaboration. Do not assume every interview is a mental-math test.

Week 1: build a baseline you can explain

Use five sessions of 60–90 minutes, adjusted to your schedule.

1. Take the diagnostic and categorize the errors.
2. Practice conditional probability with a tree or explicit sample space.
3. Work on expected value, including costs and conditional decisions.
4. Complete one coding exercise and test its edge cases.
5. Re-solve every missed problem without the answer visible.

Deliverable: a short error log and a prioritized topic list. Put your weakest recurring concept first, even if practicing your strongest topic feels more rewarding.

Week 2: add role-specific practice

For trading, use the SIG practice guide and Optiver practice guide. Explain the assumptions in each solution rather than collecting tricks.

For research, choose a project you can discuss in detail. Explain the dataset, prediction target, baseline, training/evaluation split, leakage controls, and why you trust the result. Use the research project review checklist.

For development, alternate problem-solving with implementation. State complexity, test empty inputs and boundary values, and explain the design tradeoffs. Use the languages and topics in the actual job description.

Deliverable: three problems solved clearly, one technical project explanation, and a list of follow-up questions you cannot yet answer.

Week 3: practice under a constraint

Introduce a timer to a mixed set you could solve untimed. Compare the types of mistakes you make, not just the score. Keep a short warm-up separate from the mock so you do not confuse practice with assessment.

Use this response structure when speaking aloud:

• Restate the task and assumptions.
• Choose the sample space, model, or data structure.
• Work through the calculation or implementation.
• Check an edge case or limiting case.
• Explain how the answer changes if one assumption changes.

Illustrative feedback: “The numerical answer is correct, but you multiplied probabilities without explaining independence. State the relationship between the events before calculating.” This is an example of useful feedback, not a quotation from a student's session.

Deliverable: one recorded self-mock or practice with a partner, reviewed for both reasoning and communication.

Week 4: target the remaining bottleneck

Review the employer's instructions and your error log. Spend most of your time on errors that recur across topics: confusing independence, not accounting for costs, silently changing assumptions, or failing to test code.

Practice one mock at the beginning of the week and one after addressing the biggest gap. Use different questions; repeating a memorized set inflates confidence without testing transfer.

Leave time for logistics, sleep, and reviewing your own projects. An extra late-night problem set is not always the most useful preparation.

Deliverable: a one-page readiness note listing what you can explain, what still needs work, and how you will approach an unfamiliar problem.

Keep this error log

When to get help

If you can identify mistakes but cannot explain why they recur, feedback may be more useful than another question bank. If the fundamentals are missing, a structured course or textbook can help. If your main gap is execution, keep practicing with a timer and a review loop.

Read bootcamp versus self-study to choose the level of support you need, or explore Quant Blueprint's preparation program. A free information session explains the program; it is separate from the mock interview and coaching delivered within it.