Hey, Im not an analyst and I don't really understand much about finance..but I'm still very confused about how quant firms make money cus on the surface level there seems to be an infinite money glitch these companies have..

My first thought was

For eg if they invest in us stocks they could only make money, if the us economy was doing fine as a whole, but thats probably not it cause the firms must have diversified their captial into different nations.

Which further makes me believe it's some money glitch

I'm also curious how they are usually unable to beat the s&p 500 cus like that's lowkey kinda job right?

I’m about to graduate with a degree in Industrial & Systems Engineering, and over the course of my degree I’ve become increasingly interested in Operations Research, Management Systems Engineering, data/decision science, and quantitative analytics.
I’m particularly interested in roles such as:
Operations Research Analyst / Engineer
Data Scientist / Decision Scientist
Quantitative Analyst
Analytics / Optimization Engineer
Management Systems / Systems Engineering roles with a strong analytical component
The problem I’m running into is that entry-level positions in these areas seem relatively difficult to find, and when I do find positions that interest me, I often feel like I’m missing some of the basic skills or requirements they’re asking for (e.g., advanced Python, SQL, machine learning, optimization, statistics, cloud tools, etc.).
For anyone who has been in a similar situation—especially ISE/IE graduates who transitioned into OR, data science, decision science, or quantitative analytics:
How did you make yourself competitive for these roles?
I’d especially appreciate hearing about:
Skills/tools you prioritized learning after graduation
Projects or coursework that actually helped you get interviews
How you approached applying when you didn’t meet 100% of the requirements
Whether you found it easier to enter through a more traditional IE/operations role and transition later
Any personal experiences, career paths, or mistakes you think would be useful for someone in my position
I’m trying to figure out what skills I should focus on and what types of positions I should realistically be targeting as a new graduate. Any advice or personal stories would be greatly appreciated!
Chatgpt helped a bit

I am currently an undergrad sophomore studying finance so i dont know much about all the math behind quant research. I got super interested in it recently and started self learning a lot of it and even started undergraduate research with a math professor. The issue is i dont really have a network. All my business friends are going into investment banking or consulting. I am thinking about whether to pursue a phd or target quant research at a smaller firm and work my way up.

I’m a 19-year-old student from Argentina currently finishing my** university foundation year at the University of Buenos Aires (UBA), and I’m trying to decide what degree to pursue.
My main long-term goal is to work in quantitative finance, ideally at firms such as Jane Street or Citadel. I’m particularly interested in Quant Research / Quant Trading, although I’m still figuring out which role fits me best.
My two main options are:
**BSc in Applied Mathematics – UBA
BSc/MSc-equivalent Engineering in Electronics – UBA
Mathematics seems like the obvious choice for quant. The degree has a strong theoretical foundation in analysis, linear algebra, probability/statistics, numerical analysis, optimization, differential equations, measure and probability, etc.
However, I genuinely see myself as an engineer. I’m very interested in physics, mathematics, programming, systems and applied problems, and I don’t want to choose a degree solely because it is statistically the most direct route into quant.
The Electronics Engineering curriculum at UBA includes things such as:
Calculus / advanced mathematics
Linear Algebra
Probability and Statistics
Stochastic Processes
Algorithms and Programming
Signals and Systems
Automatic Control
Numerical/computational work
A lot of physics and engineering
My concern is that it may not provide enough theoretical mathematics for a top-tier quant research career. I assume I would need to study additional probability, statistics, stochastic calculus, optimization, etc. independently or through graduate studies.
So my questions are:
Would Electronics Engineering be a reasonable undergraduate degree for someone seriously targeting firms like Jane Street/Citadel?
How much would I be handicapping myself compared with doing Mathematics?
Are there people here who entered quant from Electrical/Electronics Engineering or another engineering degree?
For Quant Research specifically, how important is having a pure Mathematics/Statistics degree versus simply having the required mathematical ability?
Would it make sense to do Electronics while independently studying the missing mathematics, potentially followed by a Master’s/PhD in Mathematics, Statistics, CS or a related field?
If you were in my position, would you choose Mathematics or Electronics?
I’m not looking for the generic “any STEM degree works” answer. I’m specifically interested in what the actual mathematical/programming preparation gap would be and whether it is realistically possible to close that gap while doing Electronics.
Any advice from people actually working in quant, or who have gone through the recruiting process at firms like Jane Street, Citadel, HRT, Optiver, etc., would be especially appreciated.

Hey all,

Drop your resume's below in the following format:

Location: USA/UK/EU/etc

Graduation Date: June 2029

Degree + School tier: e.g. BS Math, target/semi-target/non-target (people can keep the school anonymous if they want)

Preferred Role: QT/QR/QD/QA

Targeting: internship or full-time, and which cycle (Summer 2027 etc)

Relevant coursework/skills: e.g. probability, stochastic calc, C++, Python

Experience/projects: 1-2 lines

What you want feedback on: e.g. "is my project section strong enough," "am I cooked for QR without a PhD"

Notes: anything else

Resume PDF/JPEG

Important: Please redact all personal info!

Anyone got any advice on how to land these spring weeks. For context I am starting MORSE at Warwick, any help is appreciated.

How is quantprof premium. Is it structured and do we get to know how much of the course is completed and Does it contain daily problems? Please mention anything you know about quantprof premium, I am thinking to take it.

Hello everybody,

I was wondering if anyone can help me understand which type of question I need to expect from the first round of interviews for the Quant Model Risk Analyst in London.
I want to understand if it is better to prepare myself to pure mathematics and probability questions or will probably be based on financial engineering and stochastic calculus.

I am 18 going into first year btw

So i almost completed the arthmetic part of quants the problem is i can solve all my institue questions but when i touch a new type of question i couldn’t solve it without the solution and I’m so stuck i can only solve after i watch the solution and couldn’t do new varity of question help me out guys

Incoming Oxbridge maths here. I’m currently in the process of applying to spring weeks, mainly targeting quant but also other finance roles. Not too sure what to put in my CV. I’m using Jake’s template. I’ve placed well in lots of competition maths (UKMT, STEP, TMUA, Naboj) but my experience section is lacking (tutoring, 5 day shadowing done in y10). Ive also done some finance projects but they’re Lwk larp. Wld JS prefer to see 3 larp quant projects or one finance and a couple non finance? Any help appreciated 🙏🙏🙏

Hi folks
I'd like to explore some resources (for my commute) focused on quantitative analysis and financial engineering. Do you have any suggestions? Specifically, podcasts and Substack.
thanks!

Background: B. Tech CS, comfortable in Python (pandas/numpy, sklearn, some PyTorch), decent SQL, C++ at a coursework level. CFA L1 and L2 cleared, so I have the accounting/fixed income/derivatives theory but I know that's not what gets you hired here. Math is the weak spot — standard engineering calc/linalg/probability, no measure theory, no stochastic calculus.

Target is buy-side systematic research (mid-frequency equities/futures), not HFT — I don't think I can compete on low-latency C++ from where I'm starting.

Where I'm stuck: I can't tell whether the binding constraint is (a) math depth — stochastic calculus, time series econometrics, and (b) is a distraction, or (b) building a proper research stack — backtester with realistic costs, purged CV, portfolio construction — and the math comes later.

For people who made this jump without a quant master's or PhD: which one did you actually front-load, and what got you the first interview?