I'm a senior buy side quant researcher. AMA

Hi all,

As the title says, as prospective students should be making their decisions, I would like to use this platform to interact with students who might be interested in the career path.

Who Am I: I’m a senior quantitative researcher working in systematic equities. I’m what some might call a “full-stack” quant leading a team on the entire pipeline from data exploration to generating the trades that we want to do (not the actual execution though). I have been lurking on Quantnet for a few years. I did not pursue an MFE, but I did a related master’s degree and my choice for the school was informed by the rankings here. For anonymity reasons, this is not my main account.

About My Company: we are a billion$+ quantitative hedge fund dealing with all asset classes at various horizons. Our incoming researcher pool are heavily drawn from previous year’s interns.

Why Am I doing this (edited): thing can be a bit opaque from the other side, and I've enjoyed interacting with members both in this thread and in DM's. Would like to open a forum in an anonymous manner to give some perspective.

Ground rule: as long as there’s no question that can be used to identify me or my company, I’m ok to answer. I'm not going to endorse any particular schools/programs. Also, everything that I say is of my own personal opinion and there might be people with same title/responsibilities as me that have differing views. At the end of the day, I hope that what I'm saying is not totally idiosyncratic.

I'm on US Eastern time, and have a full time job so please understand if I don't answer immediately.

Pre-edit:
Why Am I doing this: I’ve been involved in our recruitment process of the last few years at all levels (undergrad all the way to experienced). As far as the process goes at our company, the chips are highly stacked against MFE students (because of how early the process starts). I personally would like to see more well prepared MFE graduates in our ranks, and I’d like to use this platform to both help students who are interested in that career path and collect some data points for my information.

 

[JAM Maddy]

Hello,

Thank you very much for starting this thread. Would you kindly shed some insights on my personal journey and provide me with some guidance/advice, please?

I have a Bachelor’s in Finance from a small state school yet AACSB accredited institution. I currently work in corporate finance as a Senior Financial Analyst using tools like Tableau, SAP Analytics, Google Sheets for Financial Modeling. I also have little bit of SQL and PowerBI experience.

For a long time (I’m 27 years young), I have been considering moving into Quant. However, I have been rejected from every single internship programs ( I believe because I have a degree from a small school). I have decided to pursue a Master’s in Data Science which I start in Fall 2023 from another small private school in PA. I would like to know if a MS in Data Science considering courses like R, Python, Statistical Modeling (SAS and MatLab) course in the program would help me get that first Quant Analyst job.

I know most Hedge Fund Companies look for students from big name schools. The reason I am going to small schools is I don’t make a ton of money and I look after my parents. So, I can’t risk taking huge amounts of debt. (Sorry shared too much information).

I would really love to know what you would suggest I do differently or change about the course of action I am about to take. Thank you very much for your advice.

Best,
J

 

[Forte]

Hi Igna,
What skills do you look for in a CV from fersher when you are recurting ?

And can you list out courses that you think can help students who are looking into quantitative research?

Thank you.

 

[GamaLapDem]

Hi all,

As the title says, as prospective students should be making their decisions, I would like to use this platform to interact with students who might be interested in the career path.

Who Am I: I’m a senior quantitative researcher working in systematic equities. I’m what some might call a “full-stack” quant leading a team on the entire pipeline from data exploration to generating the trades that we want to do (not the actual execution though). I have been lurking on Quantnet for a few years. I did not pursue an MFE, but I did a related master’s degree and my choice for the school was informed by the rankings here. For anonymity reasons, this is not my main account.

About My Company: we are a billion$+ quantitative hedge fund dealing with all asset classes at various horizons. Our incoming researcher pool are heavily drawn from previous year’s interns.

Why Am I doing this (edited): thing can be a bit opaque from the other side, and I've enjoyed interacting with members both in this thread and in DM's. Would like to open a forum in an anonymous manner to give some perspective.

Ground rule: as long as there’s no question that can be used to identify me or my company, I’m ok to answer. I'm not going to endorse any particular schools/programs. Also, everything that I say is of my own personal opinion and there might be people with same title/responsibilities as me that have differing views. At the end of the day, I hope that what I'm saying is not totally idiosyncratic.

I'm on US Eastern time, and have a full time job so please understand if I don't answer immediately.

Pre-edit:
Why Am I doing this: I’ve been involved in our recruitment process of the last few years at all levels (undergrad all the way to experienced). As far as the process goes at our company, the chips are highly stacked against MFE students (because of how early the process starts). I personally would like to see more well prepared MFE graduates in our ranks, and I’d like to use this platform to both help students who are interested in that career path and collect some data points for my information.
View attachment 47121

Hi @Igna,
Thanks for open this conversation. I´ve been trying to get in touch to a quant researcher so I can get some insight about my situation.
I´m currently studying my Masters in Mathematical Finance, and I have two bachelor degrees: one in Applied Mathematics; and one in Actuarial Sciences.

I´ve been educating myself to have the knowledge and skills that are required to enter the quant research field. From the most abstract subjects such as, measure theory, stochastic differential equations, real analysis, statistical analysis (freq and bayes), to those more applicative: time series analysis, forecasting, volatility models, and various programming languages (R, python, MATLAB). I´ve even have a decent economic knowledges both in the micro and macro perspective.

Despite my preparation, none of the positions I´ve applied to seems to be interested in giving me an interview. And I´m afraid (hope its not) its because of the following:

During my undergrad, years, I´d worked for two mainly insurance and risk management companies in the actuarial departments becasue that was the opportunities I found at the moment.

Now, that I´m trying to look for jobs that actually represents my professional interest, it came up to my mind that maybe those recruiters, in some cases, might encapsulates my profile mainly into an actuarial/insurance framework (because obviously in my CV is says what I´ve done ) and therefore, my application gets almost instantaneously rejected, without giving me even one interview.

How much of this believe could be true? And if so, should I omit my previous experiences and only send a CV with my Skills, education and accreditations?
What do you think what else could I do in order to pass at least the first filter?

Any comment or advice would be really helpful.

Thanks again for doing this!

 

[Harold_M]

Hi Igna,

Thank you so much for your post. This is a Master Student aiming to break into Quant Research.

I wonder if Probabilistic Modeling type of Machine Learning is used in the current Quant Research Scene, topics including
Exponential families, Bayesian Nonparametrics, Variational Inferences etc, or is Deep Learning Neural Nets generally preferred? Also, given the time constraint for students, would you recommend us dive deep into Time Series Analysis and learn all the details, or getting a basic grasp of the Models and knowing how to fit them through python/R is sufficient?

Thank you so much for your time.

 

[Igna]

Hi Igna,

Thank you so much for your post. This is a Master Student aiming to break into Quant Research.

I wonder if Probabilistic Modeling type of Machine Learning is used in the current Quant Research Scene, topics including
Exponential families, Bayesian Nonparametrics, Variational Inferences etc, or is Deep Learning Neural Nets generally preferred? Also, given the time constraint for students, would you recommend us dive deep into Time Series Analysis and learn all the details, or getting a basic grasp of the Models and knowing how to fit them through python/R is sufficient?

Thank you so much for your time.

(Back from a long break, and this question looks to be the easiest in the backlog )

When it comes to modeling, just knowing to do .fit()/.predict() is not going to cut it. You should strive to know about the details (the assumptions, pros and cons of the approach, when is the right/wrong time to apply the techniques, how is it implemented in practice etc). As far as ML goes, Bayesian nonparametrics is not used as much as some more basic bayesian approaches (Bayesian networks and causal inference extension are used more often). We’ve had success in applying probabilistic programming (Bayesian nets, hierarchical modeling, variational inference, etc) to doing alpha modeling. I know people strive to do deep learning but the use cases are somewhat more specialized you really need to know what you are doing to make it successful (I.e. having the right baseline, model selection methodologies, etc)

 

[Igna]

Hi @Igna,
Thanks for open this conversation. I´ve been trying to get in touch to a quant researcher so I can get some insight about my situation.
I´m currently studying my Masters in Mathematical Finance, and I have two bachelor degrees: one in Applied Mathematics; and one in Actuarial Sciences.

I´ve been educating myself to have the knowledge and skills that are required to enter the quant research field. From the most abstract subjects such as, measure theory, stochastic differential equations, real analysis, statistical analysis (freq and bayes), to those more applicative: time series analysis, forecasting, volatility models, and various programming languages (R, python, MATLAB). I´ve even have a decent economic knowledges both in the micro and macro perspective.

Despite my preparation, none of the positions I´ve applied to seems to be interested in giving me an interview. And I´m afraid (hope its not) its because of the following:

During my undergrad, years, I´d worked for two mainly insurance and risk management companies in the actuarial departments becasue that was the opportunities I found at the moment.

Now, that I´m trying to look for jobs that actually represents my professional interest, it came up to my mind that maybe those recruiters, in some cases, might encapsulates my profile mainly into an actuarial/insurance framework (because obviously in my CV is says what I´ve done ) and therefore, my application gets almost instantaneously rejected, without giving me even one interview.

How much of this believe could be true? And if so, should I omit my previous experiences and only send a CV with my Skills, education and accreditations?
What do you think what else could I do in order to pass at least the first filter?

Any comment or advice would be really helpful.

Thanks again for doing this!

This is an interesting question. I think there is a bias whenever you apply for experienced positions that they would like to see relevant experience though based on your description, were the previous experiences more of an internship? I would not necessarily omit them from your resume completely, but maybe just include 1 bullet to highlight your biggest accomplishment, and leave more room for your current coursework and preparation. However, since your experience isn’t the most relevant you might want to target the more junior positions. If you still have an opportunity to do an internship while you are doing this master’s degree then I would say definitely put your best foot forward for it.

As far as the recruiters, are you speaking with internal or external ones? In terms of internal recruiters, they can give you a more realistic picture of your chances. The external recruiters might give you an overly optimistic picture that doesn’t actually lead anywhere. However, you should try to use them to your advantage. Before sending your resume to them, verbally go over your background and have a conversation with them about things that need to be included or highlighted so that there’s a great chance of what you do send getting presented to a hiring manager. It’s not a guarantee that you will get anything out of them, but through having these conversations, you’ll get a better idea as to what they (non technical screeners) are looking for. As an aside, even if you don’t get a position, it’s worthwhile to stay in touch with recruiters that you think are good and catch up once In a while. This will allow you to keep a pulse on the job market and potential skill gaps.

 

[Igna]

Hi Igna,
What skills do you look for in a CV from fersher when you are recurting ?

And can you list out courses that you think can help students who are looking into quantitative research?

Thank you.

I usually like to see that you have a critical/inquisitive mindset and desire for depth of knowledge. We think these will allow the candidate to continue to have the drive to grow on the job. These can come from your coursework as well as the extracurricular.

I don’t really have a specific courselist that I look out for. I think I had in a previous reply a list of courses that I thought was interesting in the most recent intern interviews (tldr it spanned a lot of departments). In general, it’s good if you’ve gone out of your way to take something more challenging. Just as an example, if an undergrad took graduate level courses, etc.

 

[Igna]

Hello,

Thank you very much for starting this thread. Would you kindly shed some insights on my personal journey and provide me with some guidance/advice, please?

I have a Bachelor’s in Finance from a small state school yet AACSB accredited institution. I currently work in corporate finance as a Senior Financial Analyst using tools like Tableau, SAP Analytics, Google Sheets for Financial Modeling. I also have little bit of SQL and PowerBI experience.

For a long time (I’m 27 years young), I have been considering moving into Quant. However, I have been rejected from every single internship programs ( I believe because I have a degree from a small school). I have decided to pursue a Master’s in Data Science which I start in Fall 2023 from another small private school in PA. I would like to know if a MS in Data Science considering courses like R, Python, Statistical Modeling (SAS and MatLab) course in the program would help me get that first Quant Analyst job.

I know most Hedge Fund Companies look for students from big name schools. The reason I am going to small schools is I don’t make a ton of money and I look after my parents. So, I can’t risk taking huge amounts of debt. (Sorry shared too much information).

I would really love to know what you would suggest I do differently or change about the course of action I am about to take. Thank you very much for your advice.

Best,
J

While I can sympathize with your situation, it is unfortunately the truth that brand name schools and programs give candidates a leg up in the whole recruitment process. Your undergrad degree and work experience unfortunately are also not quantitative enough to be competitive for most real quant positions. Pursuing a masters in data science could help, but I think there are a few things you should try to make sure of:
1. The program isn’t trying to cash in on the popularity of data science. This is unfortunately the case a lot of times, and they simply aren’t great programs considering how expensive they can be
2. Take the foundational courses for quantitative finance (probability theory, stats, cs, maths). Some of these concepts get touched upon in dis programs but usually not to any depth, you’d probability need to spend some of your electives doing these.
3. Get an early sense of the recruitment process. The technical interviews can be very difficult for your background, so I’d suggest getting one of the interview prep book and start on it early to understand where your gaps are and address them in conjunction with #2

 

[JNSYU]

Hi @Igna
I'm aspiring to be a buy side quant researcher. Wish to get some guides from you on the path to it.

Some backgrounds first:
I'm an incoming 2023 MFE student. This is my 2nd master program. My 1st master is Computer Info Tech. Both programs are from ivy.
Between these programs, I have 2 years engineer experiences in a crypto company mainly doing R&D(security focused, pure tech, nothing related to quant/finance).

So few questions:
1. To prepare for the incoming internship interviews, I'm having a boot camp to review regressions, ML basics, probabilities, time series, python. Did I miss anything that is often be challenged in intern interview?
2. For my resume, do you think it's better to use more space for my previous R&D work experience or the projects from boot camps(alphas, portfolio)?
3. Do you have any recommendations of school coursework that are nice to have for a quant researcher job?
4. Companies I wish to join are Point72/Citadel/Renaissance/Two sigma. Will my background be qualified/competitive for these company after graduate? What can I do to improve?

Thanks for your time!

 

[Igna]

Hi @Igna
I'm aspiring to be a buy side quant researcher. Wish to get some guides from you on the path to it.

Some backgrounds first:
I'm an incoming 2023 MFE student. This is my 2nd master program. My 1st master is Computer Info Tech. Both programs are from ivy.
Between these programs, I have 2 years engineer experiences in a crypto company mainly doing R&D(security focused, pure tech, nothing related to quant/finance).

So few questions:
1. To prepare for the incoming internship interviews, I'm having a boot camp to review regressions, ML basics, probabilities, time series, python. Did I miss anything that is often be challenged in intern interview?
2. For my resume, do you think it's better to use more space for my previous R&D work experience or the projects from boot camps(alphas, portfolio)?
3. Do you have any recommendations of school coursework that are nice to have for a quant researcher job?
4. Companies I wish to join are Point72/Citadel/Renaissance/Two sigma. Will my background be qualified/competitive for these company after graduate? What can I do to improve?

Thanks for your time!

1. Some brainteasers could also be asked. I’d review the green book, I think it’s fairly exhaustive in terms of the classic quant question archetypes.
2. Projects from the boot camp is probably better, but be prepared to answer in depth questions. E.g. if you did portfolio optimization, what are the assumptions, drawbacks, interpretations etc rather than just saying you’ve coded up Markowitz (RIP) opt
3. The structure of MFE programs are pretty rigid so you might not have that many electives, my personal recommendation if you want to do buy side research are microstructure and (advanced) machine learning theory.
4. Renaissance aside, you should apply to the summer 2024 internship at all of those firms that’s going to give you the highest chance of a full time position. Sounds like you’ve already started to prepare so just make sure to submit the application as soon as the portals open up which can be pretty early in your first MFE semester.

Best of luck!

 

[weab]

1. Some brainteasers could also be asked. I’d review the green book, I think it’s fairly exhaustive in terms of the classic quant question archetypes.
2. Projects from the boot camp is probably better, but be prepared to answer in depth questions. E.g. if you did portfolio optimization, what are the assumptions, drawbacks, interpretations etc rather than just saying you’ve coded up Markowitz (RIP) opt
3. The structure of MFE programs are pretty rigid so you might not have that many electives, my personal recommendation if you want to do buy side research are microstructure and (advanced) machine learning theory.
4. Renaissance aside, you should apply to the summer 2024 internship at all of those firms that’s going to give you the highest chance of a full time position. Sounds like you’ve already started to prepare so just make sure to submit the application as soon as the portals open up which can be pretty early in your first MFE semester.

Best of luck!

Hey @Igna when you say “(advanced) machine learning theory” do you mean rigorous theory of machine learning or newest/most advanced techniques of machine learning? I hear that most quant firms just use linear regression.Thank you.

 

[Igna]

Hey @Igna when you say “(advanced) machine learning theory” do you mean rigorous theory of machine learning or newest/most advanced techniques of machine learning? I hear that most quant firms just use linear regression.Thank you.

The former—learnability, complexity, convergence, information theory/geometry, etc. We encourage our researchers to have a broader toolkit than what we actually use in production so even if linear regression is used other techniques were attempted during the research process. Just being able to run .fit/.predict/.score is not sufficient and they should also be able to explain why certain techniques would work better than others. A posteriori we discuss nature of the problem and dataset and think intuit about the results based on learnability/convergence/etc. If you have these under your belt, it should be trivial to learn the latest techniques on the fly and apply them to new problems in a sensible manner if needed, and it should be more easily explainable if linear regression is the right choice.

Can’t speak to other firms, but this is kind of an assumed background (either through school or self study) if our researchers want to do ML as a big part of their job function.

 

[weab]

The former—learnability, complexity, convergence, information theory/geometry, etc. We encourage our researchers to have a broader toolkit than what we actually use in production so even if linear regression is used other techniques were attempted during the research process. Just being able to run .fit/.predict/.score is not sufficient and they should also be able to explain why certain techniques would work better than others. A posteriori we discuss nature of the problem and dataset and think intuit about the results based on learnability/convergence/etc. If you have these under your belt, it should be trivial to learn the latest techniques on the fly and apply them to new problems in a sensible manner if needed, and it should be more easily explainable if linear regression is the right choice.

Can’t speak to other firms, but this is kind of an assumed background (either through school or self study) if our researchers want to do ML as a big part of their job function.

Awesome, appreciate the help.

 

[shivani.barodia]

Hi all,

As the title says, as prospective students should be making their decisions, I would like to use this platform to interact with students who might be interested in the career path.

Who Am I: I’m a senior quantitative researcher working in systematic equities. I’m what some might call a “full-stack” quant leading a team on the entire pipeline from data exploration to generating the trades that we want to do (not the actual execution though). I have been lurking on Quantnet for a few years. I did not pursue an MFE, but I did a related master’s degree and my choice for the school was informed by the rankings here. For anonymity reasons, this is not my main account.

About My Company: we are a billion$+ quantitative hedge fund dealing with all asset classes at various horizons. Our incoming researcher pool are heavily drawn from previous year’s interns.

Why Am I doing this (edited): thing can be a bit opaque from the other side, and I've enjoyed interacting with members both in this thread and in DM's. Would like to open a forum in an anonymous manner to give some perspective.

Ground rule: as long as there’s no question that can be used to identify me or my company, I’m ok to answer. I'm not going to endorse any particular schools/programs. Also, everything that I say is of my own personal opinion and there might be people with same title/responsibilities as me that have differing views. At the end of the day, I hope that what I'm saying is not totally idiosyncratic.

I'm on US Eastern time, and have a full time job so please understand if I don't answer immediately.

Pre-edit:
Why Am I doing this: I’ve been involved in our recruitment process of the last few years at all levels (undergrad all the way to experienced). As far as the process goes at our company, the chips are highly stacked against MFE students (because of how early the process starts). I personally would like to see more well prepared MFE graduates in our ranks, and I’d like to use this platform to both help students who are interested in that career path and collect some data points for my information.
View attachment 47121

Hi Igna,

This is my Background: I've recently Finished Chartered accountancy (For ref: CPA in India). I've also cleared CFA Level 1 and am currently preparing for Level 2. I've done Bachelors of Commerce with a major in accountancy and Finance.

I'm now considering going down the core finance fields and the area of Quant has intrigued. I'm debating whether to go down that route or not.

- As someone from outside the traditional field of bachelors, suitable for quant, what would be your advice with regards to upskilling in order to meet the pre-requisites as stated by University programs?

- In continuation to the question above, How difficult would it be for someone like me to crack the field of quant? Would doing courses from coursera or other such websites make up for the lack of a traditional math or CS background?

- Also, Other than simply taking courses in Math and coding languages, what other ways could be used to upskill and to be able to understand what the field of quant entails?

Thank you so much for doing this.

 

[FutureCharizard]

I finished my PhD in physics from a well known US ivy league in May 2023. My background is in computational physics, with emphasis on numerical methods for solving partial differential equations in science and engineering (all about fluid dynamics). I got my degree at 34 years old. I have no industry experience, but plenty of teaching and researching experience in academic environments.

Here are my questions:

1) As others have posted above, I'm a bit concerned that I'm entering the quant world late. Part of this feeling has to do with my worry that younger people may come with "fresher" brains and more specialized skills. I feel that competing with people 10 years younger and 10 times "faster" puts me in a hopeless situation. I ask: is age something I should really be worried about? If the answer isn't black or white, then what aspects should I be paying attention to when it comes to applying at 35+yo with no industry experience?

2) I am interested in quant jobs because I love advanced math, have a highly analytical mindset, and never surrender to a problem that needs to be solved. I enjoy taking on tasks that require both meticulous work and thorough understanding of what's going on. I also love finding ways to explain complex concepts to people not familiar with the gory details of a quantitative discipline. However, I can't see myself thinking "fast" enough for what I think (probably wrong) is a job that require fast thinking. Am I choosing a quant job for the wrong reasons? If not, are the any sub-areas in quantitative jobs that you would recommend? Maybe a different career instead?

3) Is it REALLY that important to prepare just for interviews and not worry about gathering knowledge that could be useful for the potential job I'd get? In other words, are interviews savage enough to dismiss applicants with moderate to advance, say, machine learning experience, only because they were not fast enough to answer a leetcode-style question with the appropriate time complexity under tight time constraints?

Like others, I'd like to thank you for taking the time to answer these questions. If you answer, please do so publicly, as I'm sure that I'm not the only one out there with similar questions.

Cheers,
Charmander

 

[Igna]

Hi Igna,

This is my Background: I've recently Finished Chartered accountancy (For ref: CPA in India). I've also cleared CFA Level 1 and am currently preparing for Level 2. I've done Bachelors of Commerce with a major in accountancy and Finance.

I'm now considering going down the core finance fields and the area of Quant has intrigued. I'm debating whether to go down that route or not.

- As someone from outside the traditional field of bachelors, suitable for quant, what would be your advice with regards to upskilling in order to meet the pre-requisites as stated by University programs?

- In continuation to the question above, How difficult would it be for someone like me to crack the field of quant? Would doing courses from coursera or other such websites make up for the lack of a traditional math or CS background?

- Also, Other than simply taking courses in Math and coding languages, what other ways could be used to upskill and to be able to understand what the field of quant entails?

Thank you so much for doing this.

Hi, thanks for your question. I think the path to becoming a quant will be somewhat difficult for your particular background because of the emphasis on the core quantitative areas. And, as ironic as it sounds, sometimes the traditional financial analyst background are not really appreciated in a lot of positions with a quant title. Having said that, an avenue for you to explore might be to look into a quant role at one of the quantamental firms that want to insist more quantitative methods into their investment process, or quant firms that operate on a longer trading horizon and are looking to inject more sophistication into their process.

As for your specific questions:
1. “pre-requisites as stated by university programs” does this refer to a possible MFE? If so, I’m assuming you are referring to the quant fundamental (cs/prob/stats/math). I think some of the other threads on this forum do a pretty good job of summarizing the requirements and prep involved. Please let me know if I misunderstood the question.
2. I think an MFE is the best choice here since your bachelors degree is not something that will usually get through the HR screening, and online courses vary greatly in quality that aren’t really looked favorably upon either. The other advantage other than the skills gain is the internship, which can get you fast tracked to a full time offer.
3. I think practice is the best way to solidify your learning, and get a sense of what the job entails. There are some amazing libraries people have open sourced. These maybe hobbyists or professionals, but a lot of the work is pretty good for reaching purposes. So at some point, once you’ve gotten the fundamentals from a classroom setting, I’d recommend looking at some of these libraries (GitHub - wilsonfreitas/awesome-quant: A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance) this link seems to have a nice list from a quick 30 second glimpse). Picking something that you are interested, try it out, build something unique, and possibly even contribute back.

 

[Igna]

I finished my PhD in physics from a well known US ivy league in May 2023. My background is in computational physics, with emphasis on numerical methods for solving partial differential equations in science and engineering (all about fluid dynamics). I got my degree at 34 years old. I have no industry experience, but plenty of teaching and researching experience in academic environments.

Here are my questions:

1) As others have posted above, I'm a bit concerned that I'm entering the quant world late. Part of this feeling has to do with my worry that younger people may come with "fresher" brains and more specialized skills. I feel that competing with people 10 years younger and 10 times "faster" puts me in a hopeless situation. I ask: is age something I should really be worried about? If the answer isn't black or white, then what aspects should I be paying attention to when it comes to applying at 35+yo with no industry experience?

2) I am interested in quant jobs because I love advanced math, have a highly analytical mindset, and never surrender to a problem that needs to be solved. I enjoy taking on tasks that require both meticulous work and thorough understanding of what's going on. I also love finding ways to explain complex concepts to people not familiar with the gory details of a quantitative discipline. However, I can't see myself thinking "fast" enough for what I think (probably wrong) is a job that require fast thinking. Am I choosing a quant job for the wrong reasons? If not, are the any sub-areas in quantitative jobs that you would recommend? Maybe a different career instead?

3) Is it REALLY that important to prepare just for interviews and not worry about gathering knowledge that could be useful for the potential job I'd get? In other words, are interviews savage enough to dismiss applicants with moderate to advance, say, machine learning experience, only because they were not fast enough to answer a leetcode-style question with the appropriate time complexity under tight time constraints?

Like others, I'd like to thank you for taking the time to answer these questions. If you answer, please do so publicly, as I'm sure that I'm not the only one out there with similar questions.

Cheers,
Charmander

First of all I think you are underselling yourself, you are at least a charmeleon so let’s put some wings on you. I would totally not put age against you. Most quant firms are fiercely competitive for talent and very meritocratic once you are onboard so I would not too much about being in a hopeless situation.

I think interviews and actual work are going to be very different. Yes, interviews will require you think on your feet within a relatively short period of time, but it’s much better to be thoughtful than just quick. And most of the questions come from a pretty standard suite of disciplines, that if you just practiced enough so that it’s become muscle memory I think you will be able to overcome what you perceive as being slower. Obviously it’s better to be both quick and thoughtful, but if you had to choose then I would look much more favorably at being thoughtful and correct.

In your actual work, I think what will be important is to build intuitions. At some point whatever you do will need to interact with an almost unpredictable market, so your ability to think deeply and intuitively about what is going on will be important. Now depending on what you end up doing, what you ascribe to fast thinking might be more important. If you were a trader you’d want to react to market events possibly at the minute level, but if you were a researcher, you might be a little more removed from that immediate reaction. Based on everything that you’ve said, I think being a quantitative researcher is a much better fit.

I think that last question bring some some bit of the cynicism in the hiring process. PhDs definitely get more of a pass if you can’t do a leetcode question. But more often than not is that we end up picking someone who has that medium to advanced ML experience and can do leetcode mediums. If you have some specialty to bring to the table, we will definitely consider it.

 

[bayes_stats]

Hi Igna thank you for your time. My question is what is a typical workflow of a QR from day to day and some things that are essential in his/her routine that he must master? Also if possible can you give an example of maybe a typical alpha-generating process that may have been successful? I don't want any names just want to understand and shed a bit of light on my own self-learning. I would like it if you could be thorough but if it's not possible I understand.
I also wanted to know that are there different categories of firms depending on the time horizon of the ideas and if so what would you recommend for someone who is more comfortable in generating longer time horizon alphas typically 3 - 6 months? Thanks once again

 

[alovya]

Hi Igna thank you for your time. My question is what is a typical workflow of a QR from day to day and some things that are essential in his/her routine that he must master? Also if possible can you give an example of maybe a typical alpha-generating process that may have been successful? I don't want any names just want to understand and shed a bit of light on my own self-learning. I would like it if you could be thorough but if it's not possible I understand.
I also wanted to know that are there different categories of firms depending on the time horizon of the ideas and if so what would you recommend for someone who is more comfortable in generating longer time horizon alphas typically 3 - 6 months? Thanks once again

Curious to hear the OP's response to this if they get time, but in the meanwhile check out this Reddit thread.

 

[bayes_stats]

Curious to hear the OP's response to this if they get time, but in the meanwhile check out this Reddit thread.

Thanks for the thread will check it out. Also, I would like to clarify that my question is strictly for my guidance. I will complete my bachelor's in engineering next year but I am not in a position financially to pursue an MFE afterwords. So for meanwhile, I am building my skillsets from courses available for free but the problem is don't exactly know what is required for the role except the generic Prob, Stats, Linear Algebra, DSA, numerical methods, ML and Calculus. Also particularly for ML, there are so many topics and resources that navigating it without elimination first becomes difficult. So knowing what the daily workflow is and how a typical alpha-generating process goes may help me to decide how I can progress with my learning.