Building My Quant Interview Prep Curriculum

I will be graduating in Spring 2022 with a degree in Computer Science and Mathematics from Northeastern University. Despite having had more than a year’s worth of internship experience, I have been at a slight fork in the road in terms of knowing the exact job/career path I want to specialize in.

I spent 10 months working at a FFRDC (federally funded research and development center) in Cyber Security, two months working at Dell (also in Cyber), and 4 months working as a software engineer at a well established fintech company. As of now, my ideal industry is quantitative finance as I believe the subject matter, and work environment to be a good fit for my personality type.

The quantitative hedge fund industry seems analogous to a highly abstracted game of poker where billions of dollars are on the line. Working in such an industry will allow me to merge my competitive drives (satisfied by poker) with my intelectual drives (satisfied by coding and math.) These jobs are certainly not easy to break into and demand a high degree of interview preparation. That being said, the skills tested in these interviews seem to cover the entire subject matter of my mathematics degree. Therefore, the act of preparing for these interviews will aid in the total assimilation of what I have learned these last 4 years and are a worthwhile activity regardless of my career outcome.

Based on my educational background, there are two viable job archetypes I am suited for: quantitative developer, and quantitative trader. After conducting exhaustive research I have come to the conclusion that the day to day job functions of both these roles differ based on the strategies deployed by specific funds.

Quantitative traders utilize tools built by quantitative developers to execute upon strategies developed by quantitative researchers. Traders are ocassionally extended research responsibilities and as well as development tasks depending on the organizational structure, strategy set, and size of the hedge fund.

The archetype of a quantitative developer appears to be back-end focused requiring skills in C++, Python combined with algorithm skills and well developed mathematical maturity. Additionally, database programming skills (SQL) and knowledge of networks and distributed systems seem to be in high demand as well.

The interview process for a quantitative developer focuses heavily on algorithms and data structures with bits of math brain teasers dropped in. A quantitative trader interview is the complement of a quant-dev interview: math brain teaser heavy with bits of algorithm and data structures dropped in.

I have concocted a hybrid interview prep curriculum to ensure I am prepared for both interviews. The benefit is that practicing the mathematical brain-teasers along with algorithms and data structures will prepare me for machine learning engineer interviews at big tech companies. Therefore, I believe this interview prep curriculum to be a sound hedge that will increase my market value regardless of whether I make it into a hedge fund or not.

Quantitative Developer Archetype

  • C++/Python/Matlab
  • Familiarity with machine learning concepts and models
  • Knowledge of networks and distributed systems
  • Database programming

Quant Developer Interview Prep

  • C++ specific knowledge (pointers, templates, design patterns, modern C++ concepts)
  • Algorithms and Data Structures
  • SQL knowledge (joins and ability to formulate complex queries)
  • Probability brain teasers


Quantitative Trader Archetype

  • High degree of mathematical maturity
  • Python/Matlab
  • Financial mathematics + applied derivatives

Quant Trader Interview Prep

  • Mental Math
  • Probability and Statistics
  • Miscelanious logic and math brain-teasers
  • Calculus and Linear algebra trick questions
  • Basic stochastic processes
  • Financial mathematics (options)
  • Light algorithms and data structures
  • well developed mathematical proof concepts

Study Materials

Study Plan

Module 1: Probability and Statistics + Sliding Window Algorithms

In this first module, I will do a comprehensive refresher of probability and statistics and practice sliding window algorithm questions on leetcode. My understanding of Probability is decent as I have taken multiple courses that have covered the subject matter.


Module 2: General Brain Teasers + Two Pointer Technique

The brain teaser section of both the quant prep books cover a variety of math topics. The questions test basic math but in novel and interesting ways. The two pointer technique questions on leetcode will aid in building my algorithm skills.


  • A Practical Guide To Quantitative Finance Interviews
    • Chapter 1
  • Heard On The Street
    • Chapter 1
  • Grokking The Coding Interview: Two Pointer Technique

Module 3: Calculus/Linear Algebra Review + Fast & Slow Pointers

I expect the calculus and linear algebra review to go smoothly. I recall most of this content so the preparation process will simply entail solving the questions from the prep book and actively seeking tough questions from my old textbooks.


  • A Practical Guide To Quantitative Finance Interviews
    • Chapter 3
  • Grokking The Coding Interview: Fast & Slow Pointers

Module 4: Stochastic Processes + Merge Intervals

I will likely need to refresh some of my stochastic processes knowledge from my previous coursework.

  • A practical Guide to Quantitative Finance Interviews
    • Chapter 5
  • Grokking The Coding Interview: Merge Intervals

Module 5: Finish Grokking The Coding Interview + Mental Math

At this point, I will wrap up my interview prep by finishing Grokking The Coding Interview, and aiming to hit level 99 on math-trainer.


  • Grokking The Coding Interview
    • Cyclic Sort
    • In-Place reversal of a linked list
    • Tree breadth first search
    • Tree depth first search
    • Two Heaps
    • Subets
    • Modified Binary Search
    • Bitwise XOR
    • Top ‘K’ elements
    • K-way merge
    • Knapsack
    • Topological Sort
    • Miscellaneous
  • Math Trainer: Reach Level 99
  • Practice squaring, and square roots of large numbers

Module 6: Estimations

Many quant interviews and big tech interviews have been known to ask estimation-based questions. These include ‘how much should you charge to wash every window in Seattle’ and the like. There is an abundance of questions available online for practice. These will be the final and lowest priority aspect of my interview prep.

Pranav Ahluwalia

My name is Pranav Ahluwalia. I am a software engineer and avid poker player
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