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In quantitative finance I hear a lot of students say, "I love money" implying that will make them good at quant finance. It is a very shallow statement and one that shows the student hasn't thought much about that statement. The students that do truly love money for the sake of money are shallow people that won't make it in quant finance. Now that being said, I think many students making the statement either find economics interesting which covers how money transfers value, stores value, and how people create value. And or they love what money can do for them. Money can buy you things such as experiences, assets, or even provide financial freedom which is why people save or invest for retirement.

So before you tell someone you love money, think twice. Communicating why you would be a good fit for finance or quant finance has nothing to do with loving money. Those that excel at quant finance do so because they love stats, math, and understanding how the world works.

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The biggest lie in quant finance is that you can do everything required at the highest level of a model. This includes data engineering, model development (quant research), implementation (quant dev), and be the end user (often a trader or operations team). This full stack quant does exist however they are never the best at everything and they often work in a very specific niche. Examples of a niche could be, auto loans, equity HFT, commodities, securitization, or etc. The systems, languages, math, stats, financial theory, and business uses are all different.

I would encourage people to focus on one main area (data engineer, quant, quant dev, or business user) and dig as deep as possible. There are a lot of topics inside of each of these and it is easier to jump around business types than to try and cover too many areas with non-overlapping skills. 

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For this Thanksgiving season I would like to express my gratitude for my dad. My dad was a traveling salesman and the reason my mom was able to be a stay at home mom. The one skill that I am most grateful for is my work ethic. I learned to work along side my dad as a kid growing up. The example my dad set helped engrain in my the need and desire to work. I spent a lot of my childhood working on our house, cleaning cars, or traveling across the country for my dad's job.

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As Thanksgiving is this week I wanted to reflect on my gratitude for my mom. My mom was a stay at home mom and was lucky enough to be able to stay home and spend a lot of time with me as a child. Many of the life lessons around manners, religion, and domestic skills such as cooking and cleaning all came from my mom. My mom's attitude towards education is also what kept me somewhat engaged as a child with school even though I did not like attending when I was a kid. Having kids of my own has also helped me realize the amount of time, effort, and pain it takes to raise kids. For these reasons I am very grateful for my mom and the effort she put into me.

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Understanding performance reviews is challenging especially if you have not worked at a variety of firms. There will always be a human judgement portion which is not a hard science. That being said, reviews should be very specific to each position. When I moved from an individual contributor to a manager role, I was not happen that my annual review was average. Over the last 5 years I had always performed above average. What I didn't understand was that I was now being judged and reviewed as a manager. Did I build better models or conduct better validations than most of the team? Yes. Was I more productive? Yes! Did my employees improve on their performance? No. It was my job to perform individual contributor work well however it was now also my job to get my employees to increase their performance. As a team were were average. Managers are judged by team performance just as much as individual performance.

My best advice is to really look inward and figure out what you need to work on. This is always hard as we view ourselves differently than managers but this will help you become a better employee and person.

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Many things make legends who they are. It seems the commonality is the ability to perform at a very high level over an extended period of time. Looking at Fischer Black, Peter Carr, Rick Rubin, George Box, Gilbert Strang, and John Nash it is interesting to see so many difference between them yet they have all had significant contributions to their area of expertise. Soft skills seem to play some part of this title of being a legend. There are many people who made one contribution but seem to have faded and forgotten.

For quants it seems one must contribute academic ideas as well as have some sort of unique difference that helps others. Peter Carr's legacy seems to be amplified by his generosity of time and ability to teach. I would argue the success of making money contributes very little to being a quant. Investors and traders are very different than quants.

Rick Rubin Interview (Joe Rogan):
https://open.spotify.com/episode/5oDyN0CFjEwClqjZxJQf9O

Gilbert Strang Interview (Lex Fridman):
https://youtu.be/lEZPfmGCEk0?si=G5K6HpS4LkftujCh

MIT Course - Gilbert Strang:
https://youtu.be/ZK3O402wf1c?si=m8QWrXTeVWVi086G

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As grad school programs and undergrad programs are starting up it is important to get the most out of the time you are there. School seems like it goes slow however by the time you graduate and have been working a few years it seems like long ago.

1) Network with a wide range of students.
2)  Do informational interviews with alumni and industry practitioners to learn what specialty you want to work in after graduation.
3) Lean In! It is very challenging to balance the social aspects of grad school with the educational aspects. Focus more on learning and networking instead of optimizing your grades. Realize you will be graduating soon and will not have the same amount of time to really focus on your education. 

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As a book review I give "Operation Paperclip" 5/5 stars! I'm not typically a big fan of popular paperback books (non textbooks).


The dilemma in the books shows a brief insight into some of the horrible killings and crimes as well as the potential or perceived need for the science to end WWII with Japan as well as fend of Russia (cold war). Determining who was of value was challenging and many of the crimes were swept under the rug. As someone who practices science it is understandable that we need break throughs especially when war is involved. However on the other hand the true Nazis needed to be held accountable.

Science funding fell off in the US and it seems to be negatively impacting progress in both the public and private sectors.


I meant Eric Weinstein not Bret Weinstein however they both have a similar stance. Eric states more of the details of the lack of funding historically.


My Life as a Quant:
https://amzn.to/3Kn3h9R (my affiliate link)

Eric Weinstein:
https://open.spotify.com/episode/7MDxyrrhD7gC7XMRwB0ulv

Bret Weinstein:
https://youtu.be/pRCzZp1J0v0


Website:
https://www.FancyQuantNation.com

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https://www.teespring.com/stores/fancy-quant

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https://www.linkedin.com/in/dimitri-bianco
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I met Christina Qi virtually at the Princeton Quant Conference in 2023. I was really impressed with her presentation on hedge funds, data, and career advice. I connected with her on LinkedIn and found her posts insightful as she explains a lot about what she is doing career-wise even when it isn't the trendy thing to do. I invited her as a guest on the podcast to learn more about her and her career. One of the best points she makes in this interview is the importance of work-life balance.

If you want a more complete picture of her background, check out her LinkedIn profile.

Christina's LinkedIn
https://www.linkedin.com/in/christinaqi/

Data Bento:
https://databento.com/

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The finance industry seems to be following the social movement of adding bias and personal opinions instead of allowing the invisible hand of capitalism to properly assign and balance resources and outcomes.

Consulting firms are now coming up with politically driven definitions of bias instead of using the mathematical definition and customers can choose their definitions. From a logical standpoint, choosing a definition is the opposite of non-bias. This approach of choosing your definition means models will be biased in the direction a firm wants even when the true data does not support it. This will have negative impacts in finance (fair lending practices), marketing campaigns (firms trying to push their narrative), and I'm sure other areas I can't think of.

The new mortgage proposal to redistribute wealth from financial responsible people to irresponsible people is another example of the push towards socialism and away from capitalism. This new rule reminds me of the 2007/2008 mortgage crisis in the sense that people are trying to get those not financially stable to buy houses they can't afford. I remember the logic that if the requirement to have a down payment on a house was removed, we would get the "historically unable to purchase a home" into houses. The not surprising result was that people who couldn't afford homes bought homes and then defaulted. This new rule will have similar results. You can't fake financial stability.

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