Timothy Crack

The #1 quant interview prep book. Every probability puzzle you'll face. Do every problem twice, timed. The single highest-ROI book to buy.

📖 Part I: Brainteasers, Part II: Probability

Xinfeng Zhou

The 'green book'. Stochastic calculus, probability, options — the canonical resource for Jane Street / Two Sigma phone screens.

📖 All of it. Especially probability and options chapters.

John Hull

'Hull' — the standard derivatives textbook. Essential for any trading role that touches options or fixed income.

📖 Chapters 1-13 (options fundamentals), Ch 17-19 (Greeks, Black-Scholes)

Nassim Taleb

Deep practical treatment of options trading, volatility, and risk. Taleb's insights on fat tails and convexity are fundamental.

📖 Part I (market making), Part III (volatility)

Wilmott, Howison, Dewynne

Rigorous mathematical treatment of derivatives pricing. Excellent for anyone targeting mathematical finance or structured products roles.

Harvard Stat 110

Free PDF + YouTube. The best probability course in existence. Conditional expectation, Markov chains, and generating functions are all here.

📖 Lectures 1-24, all problem sets

Paul Wilmott

Encyclopedic 3-volume treatment of quant finance. Use as a reference — not cover to cover. The volatility and numerical methods sections are particularly good.

Frederick Mosteller

The classic collection of creative probability puzzles. Every problem requires insight, not just formulas. Problems require multi-step reasoning and often have elegant surprises. Problems 1–25 are the most interview-relevant.

📖 Problems 1–25; especially Gambler's Ruin, The Collector's Problem, Flipping Pennies, and the Inspection Paradox

Mark Joshi

Written specifically for trading desk and quant research interviews. Goes beyond brainteasers into derivatives pricing and stochastic calculus with fully worked answers — the closest thing to an answer key for real quant interviews.

📖 Ch 2–4 (probability, statistics, finance), Ch 7 (programming), Ch 8 (brainteasers)

Jane Street Capital

Monthly competition-level puzzles published since 2014. Extremely hard, multi-step — the exact style and difficulty tested at Jane Street, Optiver, and IMC. Study past solutions carefully to understand how experts break these down.

Brainstellar

The best-organized probability and brainteaser bank online. 200+ problems categorized by difficulty, type, and topic, each with detailed solutions. Filter by 'Hard' for interview-level content. Firm-tagged problems available.

QuantGuide

Dedicated quant trading interview prep platform with firm-tagged problems, timed drills, difficulty ratings, and full solution walkthroughs. Covers probability, mental math, and market making for Jane Street, Optiver, IMC, Citadel, HRT, and more.

Quantnet Community

Real interview reports submitted by candidates at all major quant firms. Search by firm name to find actual questions asked in recent interviews and how they were solved. Invaluable for firm-specific targeted prep.

Antti Ilmanen

The factor investing bible. Every anomaly, every explanation, every data source. Required before any quant research interview.

📖 Part I (building blocks), Part III (equity factor premia)

Grinold & Kahn

Defines the language every quant researcher uses: IC, IR, breadth, transfer coefficient. The Fundamental Law of Active Management originates here.

📖 Ch 5 (Fundamental Law), Ch 6 (forecasting alpha), Ch 7 (portfolio construction)

Marcos Lopez de Prado

The modern quant research bible. Cross-validation for time series, feature importance, and backtesting are completely rethought.

📖 Ch 7 (CV), Ch 8 (feature importance), Ch 11 (backtesting)

Harvey, Liu & Zhu (2016)

Establishes the t ≥ 3.0 standard for factor significance. Know this argument cold before any quant research interview.

Kakushadze & Zureick-Brown (WorldQuant)

Free on SSRN. 101 real alpha expressions used in production. Study every one — they teach you how practitioners think.

Chincarini & Kim

Best book for Barra-style factor models from theory to Python implementation. Covers factor exposure estimation and risk decomposition.

📖 Ch 1-6 (factor models), Ch 9-10 (portfolio construction)

Ernest Chan

Practical backtesting from scratch. Best resource for learning pitfalls by building real backtests.

📖 Ch 3 (pitfalls), Ch 5 (interday strategies)

Bailey & Lopez de Prado (2014)

Corrects Sharpe for selection bias across multiple backtests. Cite it when discussing strategy evaluation.

WorldQuant

Free platform to test alpha expressions on real data with instant IC/Sharpe feedback. Spend 10+ hours here before any WorldQuant interview.

Dartmouth / Ken French

Free daily/monthly factor returns since 1926. Primary validation dataset for all factor research. Download and reproduce FF3 results.

Chip Huyen

The definitive ML system design book. Read cover to cover. Required before any senior MLE or Applied Scientist interview.

📖 Ch 4 (training data), Ch 7 (model deployment), Ch 8 (data distribution shifts), Ch 9 (continual learning)

James, Witten, Hastie, Tibshirani

Free PDF. The best applied ML textbook. Chapters 2-8 cover everything tested in MLE and Applied Scientist interviews.

📖 Ch 2-4 (statistical learning, regression, classification), Ch 8 (tree methods)

Andrej Karpathy (YouTube)

8 videos building GPT from scratch. The single best resource for understanding deep learning from first principles. Watch all 8.

Goodfellow, Bengio, Courville

Free online. Mathematical treatment of neural networks. Chapters 6-9 (feedforward, regularization, optimization, CNNs) are the core.

📖 Ch 6-9, Appendix on linear algebra and probability

Vaswani et al. (2017)

The transformer paper. 15 pages. Know every design choice: why scaled dot-product, why multi-head, why positional encoding.

Ouyang et al. / OpenAI (2022)

The original RLHF paper. Know the SFT → RM → PPO pipeline cold. Required for any AI lab interview.

Hoffmann et al. / DeepMind (2022)

The scaling laws paper. Know: N_opt ∝ √C, D_opt ∝ √C, GPT-3 was undertrained.

Hu et al. (2021)

Parameter-efficient fine-tuning. Know: hypothesis, B=0 initialization, scaling factor α/r, and limitations.

NeetCode

150 LeetCode problems grouped by pattern with video explanations. The single best structured coding interview resource. Do all 150.

Meta AI

State-of-the-art tracking with linked code. Track benchmarks, find recent papers in your area before interviews.

Luciano Ramalho

Advanced Python internals: generators, context managers, concurrency, object model. Required for senior MLE roles.

📖 Part II (data structures), Part IV (control flow), Part V (metaprogramming)

Lilian Weng

Outstanding summaries of every major LLM and RL topic. 'LLM Powered Autonomous Agents', 'RLHF', and 'Diffusion Models' are all essential.

StrataScratch

Best platform for Applied Scientist-level SQL and Python questions. Filter by company (Amazon, Meta, Google) for real interview questions.

Kohavi, Tang, Xu

The A/B testing bible. Chapters 1-8 are required before any Applied Scientist interview. Written by the teams who built Microsoft/Airbnb experimentation.

📖 Ch 1-4 (fundamentals), Ch 7 (variance reduction/CUPED), Ch 21 (the dirty dozen pitfalls)

Dao et al. (2022)

IO-aware attention implementation. Know: tiling, O(n) memory instead of O(n²), 2-4× speedup. Required for any LLM deployment role.

Larry Wasserman

The gold standard for statistics in quant and ML interviews. Read Chapters 1-9 on probability, estimation, and hypothesis testing.

📖 Ch 1-9 (probability, statistics, estimation)

Community

Search '[Firm] quantitative researcher/MLE/applied scientist interview'. Real questions from recent candidates. Do this for every target firm.

Scott Cunningham

Free online. The most accessible causal inference textbook. DiD, RDD, IV — all covered with clear examples.