Marek Šabata (October 2017)


Marek Šabata comes from Prague and finished his bachelors studies at IES in 2014. A year later he graduated with another bachelor degree from the Faculty of Mathematics and Physics, in the field of Pure Mathematics, and in the same year he enrolled in a Masters degree at Columbia University in New York, where he focused on Financial Engineering. Here he earned a Master's degree this spring.

Marek’s professional interests are Optimization, Artificial Intelligence, Computing, and Large Scale Data Analysis. He participated in several interesting research projects such as Structured Nonlinear Compressions for Supervised and Unsupervised Learning, or Subway Challenge. In the latter, he utilized various graph algorithms in order to find the shortest time path between all stations in NYC Subway.
Marek currently works for Goldman Sachs as a Quantitative Strategist. He is mainly focused on applications of machine learning in Equity Capital Markets. He also gained experience as a Quantitative Researcher at Morgan Stanley Investment Management in New York, and and before that on Data Analytics positions at KPMG and IBM in Prague. Marek spends his leisure mainly by being physically active. He plays golf, runs, does CrossFit and is a volunteer dog walker.


In addition to the IES, you have also studied Bachelors at the Faculty of Mathematics and Physics. Why was this school mix attractive to you and how did you manage the combination of two challenging disciplines?

Ever since high school I was fascinated by math but at the same time I didn't want to become pure mathematician and rather wanted to focus on its applications. For that reason, I decided to attend IES as it offered rigorous courses in Mathematics while combining them with interesting applications in Economics. However, the extent of math courses at IES is - understandably - limited and my zeal for math wasn't satisfied. Hence, I decided to enroll in the Pure Math degree at MFF the following year and studied both IES and MFF.
Economics and Mathematics are not entirely distinct fields - both are very analytical and share some common topics. Moreover, I found all the math classes at MFF incredibly interesting (well maybe except general algebra :)) and I fervently enjoyed my time at MFF. Thus, I never found the coursework at IES and MFF to be overwhelming. Also, as the (pre)exam period is 5-6 weeks long it was possible to pass 8-12 subjects per semester at Charles University. This wouldn't be possible e.g. at US universities where all midterm and final exams are within one week.

You went to Columbia University for a Master's degree. What did most influence you in deciding on school choices, why Columbia? Didn’t you like any European university?

Since my teenage years, I was determined to study or work in the US. Studying abroad was another reason why I decided to enroll at IES as it's known for consistently sending many students to top academic programs abroad.
After finishing my bachelor at IES, I had one year left at MFF, so I started preparing for Masters abroad.
My enthusiasm for math and its applications further grew throughout my studies. Specifically, I developed keen interest in Statistics, Optimization, and Operations Research (OR) in general (even though I still find Functional Analysis to be the most beautiful and fascinating area in math). However, Operations Research is a very broad field, so I needed to narrow down my search. At that time, I believed that one of the most interesting applications of OR are in finance, which led me to financial engineering (FE). I started researching Masters in FE and I quickly realized that due to the sophistication of US financial markets and the job market respectively, US schools offered top education in that field. In Europe there are just few FE or FE related degrees and none of them particularly struck my interest.

When it came to narrow down the programs, my top priority was a curriculum fit. In this regard Columbia's program was easy first pick as it offered six well-structured core courses and I could pick six electives, which proved to be crucial in my career development as I could focus on Machine Learning, Optimization, and AI apart from finance.
My second priority was where the program ranks in terms of job prospects and quality of its faculty members. In this regard Columbia was also among the top picks. Exactly those two factors are why Columbia's Financial Engineering program consistently ranks among the top 3 in the world.
Finally, the NY location was a significant factor as you are essentially in the financial center (and really the capital city) of the world.

Note: You can read more about my studies at Columbia at IES webpage in the Study Abroad section.

You have experienced internships in Prague at IBM and KPMG and in NY at Morgan Stanley and now you work at the Goldman Sachs. How do you evaluate the work in these companies? Does the work environment or business morale vary from one continent to another?

Even though the companies/divisions where I worked are quite different, overall the work environments are comparable. I believe it's given by the fact that all the companies are large corporations which, to a great extent, determines the work environment and how they operate.
Regarding the work itself, I've been involved in interesting projects on both continents and met very talented people in all companies I've worked for. The concentration of hard working, motivated, and highly talented people is naturally higher in NYC. Having said that, I believe that most of the colleagues I worked with in Prague would've been equally successful in NYC if they had the opportunity. In genera,l work hours in NYC are longer than in CZ which corresponds to the size and level of the business you do.

Directly before your engagements in GS, you have been a research assistant in Columbia. What exactly did your research focus on? How did you move from research to business?

I was working on a research in Market Microstructure with Prof. Costis Maglaras. At first, the research was conducted as a project for one major Family Office Hedge Fund in NYC. We were supposed to develop electronic trading platform system, but, unfortunately, the Hedge Fund decided not to go forward with the project in the end.
In my opinion, Market Microstructure is one of the most interesting areas in financial engineering and finance in general, as it brings together fields such as economics (exchanges and how people interact), machine learning, stochastic optimal control, programming, and finance (markets knowledge).
Once the project was halted, I decided to take the opportunity at Goldman Sachs where I now work as a Quantitative Strategist in the Investment Banking Division. Strategists are responsible for developing state of art quantitative and analytical methods to help identify and solve business problems. Even though I work in the Investment Banking branch of GS, I spend most of my time working on machine learning algorithms and other approaches dealing with algorithmic problem solving.
Large portion of the work is consisting of doing research, i.e. trying new things and seeing if and why they work or not, so the transition from research to business wasn’t that palpable. There are differences though – in the business environment you usually have given deadlines and the focus is on helping to generate revenue in the end, whereas in academic research you have more creative freedom and time to be very thorough.

I was surprised that you are doing CrossFit and walking dogs. It's an interesting combination :-) How can I imagine the “dog walking”, is it a voluntary work or is it really just a hobby?


I've been doing sports my whole life and would probably go mad if I stopped regularly producing endorphins by working out. So, I do CrossFit in the mornings before I go to work as that's the only time you can schedule in the work out.
Dog walking is purely a volunteering activity. I love dogs and I've been around them as long as I can remember. However, it's hard having a dog in NYC as you usually don't spend much time at home and you can't bring him/her (though that'd be awesome - just my productivity would take a sharp downturn). To remedy my dog heart ache, I started going to dog shelter in Williamsburg during weekends, where you can walk dogs before they get adopted. Luckily, the turnaround is high and most dogs find furever homes in few weeks.





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