After learning the ropes of college and securing a 4.0 my freshman year, I jumped this summer into the fast-paced life of working in NYC. My working hours were devoted to working at Chartbeat while any remaining time was dedicated to HackNY and the events and social good projects they stewarded.
The first week at Chartbeat went incredibly well, plunging me straight into meaningful and fulfilling work while also introducing me to the people and systems that make up a high stakes startup. Within a day I was completely set up with my development and professional environment to contribute fully to their code base. In the first two weeks, I had already built my first full feature and had it fully ready for deployment straight to production.
This experience kept up the entire time with each two-week sprint leaving me fulfilled and executing a real impact on the product. My work was a major part of the launch of their historical analytics product out of Beta and I was able to contribute at every level of their stack from underlying C code for a custom database to the React and Angular front-ends powering the site.
The world of tech is diverse but many people choose to focus on getting a job at a limited number of massive tech companies (Google, Apple, Microsoft). However, for starting out in the tech world, these companies may not delivering the best learning experience and are unlikely to help one divine ones personal passion and the specific kind of tech work one enjoys the most. This is because these companies are very segregated with dev ops separate from their ML engineers with this pattern continuing between any sub-discipline.
At Chartbeat, this divide didn’t exist with only about 35 engineers. Each team had at least one person from each technical discipline and through this, I got an incredible look at each technical area and was able to make productive contributions to each of these verticals as well (from writing CSS styling for the front-end to coding an interface for an LRU-Cache in Clojure for deduplication).
I strongly suggest students seeking technical experience look for places that can give them this level of breadth as one will benefit immensely from it and can then go on to specialize with the certainty that you are making an informed decision about the track you want to pursue.
I rigorously stuck to my running schedule, exploring much of Brooklyn and getting an unprecedented view into the vibrant life of middle and lower Manhattan. While at Columbia during the academic year, just reaching Brooklyn is already almost a 9 mile run one way, so full running exploration is a daunting ordeal, but, by staying on Union Square, exploring the area was (although still covering impressive distances) comparatively easy.
I also enjoyed some of the cuisine New York has to offer along with friends. Ramen Thukpa was a fun ramen place with hearty broth and tasty toppings and Chinatown was only a short walk away so there was no end to the options.
Last but definitely not least, I met some of the cast of The Expanse (a television I adore for its incredible depiction of a future colonized solar system within our technical reach). This was an amazing time and occurred at the airing of the final episode along with a big accompanying celebration (and a raffle during which I won a hand-made Tycho coffee mug).
Part of HackNY this summer was a social good component. HackNY partnered with other organizations and people to find opportunities where our skills could be best used. I was involved with two projects and was able to contribute after work hours and during the weekend.
For the first project, I worked to parse millions of Persian documents to give the needed information for journalists to expose the corruption and sanction violation the Iranian government engages in. This involved developing a distributed NLP parsing system to rapidly analyze the free-form Persian documents and extract names, business numbers, and IDs.
My second involvement was in developing a machine vision system to parse faxed bank records in sex trafficking cases. The NY office receives so many bank statements a human analyst can’t transcribe them all so this tool should be invaluable in helping enhance their prosecution capability. This involved employing an OCR system coupled with an inference engine to correct for the numerous errors the OCR code perpetrated.
At the end, it felt like the summer had blown by in terms of time, but the quantity of output and productivity were more than satisfying. The summer ended with a bang as I presented on my time there and learned about the incredible diversity of experience my peers thrived in. I would highly recommend the HackNY fellowship and employment at Chartbeat to any computer scientist with similar passions.