Growing up I was always interested in science. In the fifth grade, I remember working on a science project that involved making homemade glue. In middle school, I spent a month reading about glacial erosion and built a small demo to present to my earth science class. In 2005 I started school at Midwood High School in Brooklyn. In my sophomore year, I took AP chemistry which set the course for my subsequent academic and professional career path. I was obsessed with chemistry. The rules of chemistry made logical sense and required less rote memorization than other science courses. During my sophomore year of high school, I took effort to get involved in the available science programs at my high school. Midwood offered programs like Medsci and the Intel Science Program, both of which I joined. Medsci was for students who planned on being sentenced to premed and Intel was for students who wanted to work in research labs.
Through my participation in Intel, I started volunteering at an organic chemistry research lab at Long Island University (LIU). While there I worked on the synthesis of a chemotherapeutic agent, Prunustatin A, though to no success. Funny enough, I saw that a complete synthesis of this drug was published in 2015 (6 years later).

In 2009 I started undergrad at Brandeis University where I studied chemistry with a minor in math and a self proclaimed minor in physics. I ended up writing a senior thesis on the synthesis of N-heterocyclic carbenes (NHC) for small molecule activation of greenhouse (GH) gases. The idea was to use these NHC transition metal complexes to capture GH gases like methane and carbon dioxide for clean energy use.

This led to a second author publication (I know, practically a coronation). Though my focus was on synthetic chemistry while in undergrad, I developed a strong interest in math and physics which led me to pursue a Ph.D in chemical physics at Cornell University in the Ananth Group.
While at Cornell I worked on the development of mathematical models used to simulate the process of photosynthesis. I mostly did pen and paper math and coded in Fortran. I studied quantum mechanics, classical mechanics, quantum chemistry, statistical mechanics and went down so many abstract math rabbit holes it was like a scene from Us.
While I enjoyed my time at Cornell, I knew early on I wanted to pursue a quantitative discipline in industry. I loved the work I did in academia, but I simply didn’t feel that my work was having any impact on the real world. The method I worked on was an extension of an approximation to quantum dynamics using the path integral representation of the quantum Boltzmann distribution. Exactly.
Or put more succinctly:



About 5 people in the world (including myself) care about the equations above. So upon graduation, I knew I wanted to have a job that had some realizable impact on the real world. Given my quantitative background I considered quantitative analyst and data scientist roles in industry.
The process of transitioning from academia to industry wasn’t the easiest. Upon graduation I stumbled through a series of failed interviews for quantitative analyst positions, quantitative researcher positions and Data Science positions. Despite how much I learned while in graduate school, I just didn’t have enough experience with python and data science to do well in interviews. My knowledge of Fortran and Feynman path integrals just wasn’t cutting it. In industry Fortran was like latin, I could only use it to be annoying and Feynman path integrals can only be compared to violence.
I tried my best to juggle dissertation writing, research and self-teaching myself data science. I knew the basics of data science and python but I just didn’t have enough applied experience to do well during interviews. There were moments in interviews where I felt like Charlie from It’s Always Sunny in Philadelphia, preaching Bird Law.
After 2–3 months of failed interviews I finally nailed down a data science position with a start up in New York. While it was exciting that I landed a job, the circumstances weren’t ideal because I was essentially working for free with the promise of future pay and equity. Going in I knew that financially it was not a great choice but professionally it couldn’t have been a better choice. I worked at a start up developing a tool for detecting fraud within cryptocurrency trades. The job was fast paced and high pressure which forced me to learn python and data science methods quickly. After 6 months of work I developed a fraud detection system that generates alerts for market manipulation in real time.
While working at the start up I was actively looking for another job. By month 6 of working there the value of "maybe someday I promise probably equity" was close to zero.
Eventually, my former chemistry advisor from LIU put me in touch with his former coworker who was recruiting researchers for another start up. After a series of interviews screening me for personality, knowledge of data science, and a python Programming test I received an offer. I was elated. Having worked for free 6 months I felt extremely validated by my hard work and professional growth. I was hired at a company that values my current skill set and was invested in my future professional growth. A year and a half later I have much more experience under my belt. While I still have a tremendous amount to learn, I am very grateful to both of these start ups for giving me a chance to grow and learn when no one else was willing. In hindsight, I should have taken greater measures to develop my python and data science skills while in graduate school. In any case, I felt accomplished in facilitating this transition and greatly empathize with anyone going through a similar process.