Intermediate Data Scientist – Network Analysis & Control at MITRE
What inspired you to pursue a career in data science and analytics?
As an undergraduate student at the University of Wisconsin-Madison, I spent a lot of time figuring out what I wanted to pursue as a major. I came into college expecting to be an environmental science major but quickly realized it was not for me, I then shifted to Atmospheric and Oceanic Sciences, then Actuarial Sciences, and then Mathematics, ultimately realizing that what I loved the most was making meaning from numbers across all subject areas. Eventually, this led me to a statistics major. And what is modern-day statistics applied to big data… Data Science!
How did you find yourself focusing on using data to help create a safer world?
Growing up so close to Washington DC “social good”, “security” and “government” were pretty much household terms. When I heard about all the great data-related work various non-profits were doing foreign and domestic, I knew that I wanted to leverage what I learned in the Data Science & Analytics programs at Georgetown in those areas. Whether it be sampling data to create food wastage mitigation programs or leveraging network analytics to reveal potentially nefarious entities, social/societal impact means the most to me.
What has your career journey looked like since you graduated?
Shortly after graduation, I started working as an Associate Data Scientist with the MITRE Corporation in the Network Analytics Department, I was placed on a variety of both MITRE’s internal projects and projects for a specific Department of Homeland Security (DHS) sponsor.
Soon after my one-year anniversary at MITRE, I was promoted to Intermediate Data Scientist – Network Analysis & Control within my department. I was also given the opportunity to present at various Technical Exchange meetings for projects that I was now doing for a variety of different government agencies. And in January 2022 I became a technical lead for a MITRE innovation project.
Starting in the fall 2022 semester, I will be an Adjunct Professor for DSAN’s Analytics 511 course (Probabilistic Modeling & Statistical Computing) in addition to continuing my work at MITRE.
What was the best career advice you received while in the DSAN program?
Probably a tie between “start early” and “leverage LinkedIn”. Starting early gave me peace of mind that I was doing everything I could to secure a job after graduation. Although expensive, I personally found value in LinkedIn Premium because of its applicant comparison analytics and application viewing priority.
How do you use what you learned in the DSAN program at work?
Part of my day is spent coding and part of my day is spent reporting/presenting. Aside from leveraging machine learning and big data analytics on a day-to-day basis, I simply work to code efficiently, in conjunction with presenting and visualizing the findings of advanced analysis in a way that sponsors/stakeholders can understand and help solve problems for a safer world.
Technically, in my first year working I leveraged data visualization of massive data, analysis of open-source intelligence (OSINT), and natural language processing (NLP). In the past year, my technical work included Cloud/High-Performance Computing, Spatio-Temporal Data Analytics, and Rule-based Un-supervised Machine Learning like Association Rule Mining and Correlation Analysis.
What were some of your best experiences while in the program, both inside or outside of the classroom?
Until the pandemic brought everything online my favorite experiences were the DCR conference hosted by Lander Analytics at Georgetown University, getting Wisemiller sandwiches between classes, and group study sessions in Lauinger library.
What was your favorite DSAN course at Georgetown? Why?
All the courses I took at DSAN were amazing, but my favorite course was Massive Data Fundamentals (ANLY 502). Although it was new in terms of content adding to the challenge, it was a lot of fun, and the completion of every assignment was very satisfying given all the moving parts with regard to running AWS clusters and running programs in distributed environments. I think what stuck out to me the most was the fact that the scale and processing power used in the class were very applicable to real-world data streams.
Why did you choose the DSAN program and why would you recommend it to prospective students?
I chose this program for 3 main reasons. First, this program was the only one to have separate courses for the data science areas I was most interested in coming out of undergrad: Machine Learning, Massive Data analysis/processing, and Data Visualization. Second, the DSAN program gave me to opportunity to work part-time, TA, and get involved with research affording me the ability to get the true graduate school experience. Third, having grown up in Northern Virginia, Georgetown University was always known as a world class school with a great reputation. Those three main reasons combined made this program the obvious choice!
What are you reading/watching/playing/listening to/learning right now?
In my free time, I like to read philosophy, play the guitar (mostly finger-style), play squash, make pizza, play with my cat, and watch The Fast and Furious movies. I cannot wait for Fast X! Recently I bought a small electronic drum set that I use to make beats and improve my rhythm.
Principal Solutions Architect at Amazon Web Services in Strategic Accounts
It took me two and half years (5 semesters) to complete the 10 course (30 credit) program. I graduated with a GPA of 3.97. This would not have been possible without the support of family. My wife was basically a single parent for our two sons (now 9 and 5) for a good part of the two years [ that I was in school]. My sons, who initially could not understand why was it that papa was the one who was always studying whereas they were the ones who were in school, ultimately convinced themselves that papa studies because he loves doing homework!
For me, the decision to go back to school and that too graduate school wasn’t exactly a planned or even a well thought of decision. It was just something I went ahead with and somehow one by one things started falling in place as if this was something that was always meant to happen. There is this line from a Bollywood film that summarizes my thought process (or lack of it) in this case “सोच गहरी हो जाये तो इरादे कमज़ोर हो जाते हैं”, Google translates this (rather literally, but aptly) to “when thoughts become deep, the intentions become weak“. I gave little thought to how would I manage time between family, work and university, cost of the program and also would I be able to cope up with the hard academic requirements given that last time I was in a university was 16 years ago (it was the year of the Y2K bug) and in a different country (India).
The semesters whizzed past one after another, each 12 week or so semester was like one of those super high speed rides in an amusement park, you know that you are going to survive and that it will be over before your heart fails, so just hang in there. I usually had two (sometimes three) classes a week, the rest of the weeknights were all homework nights, weekends were homework days and nights. Georgetown being a Jesuit institution is extremely academic, written mid term exams, take home mid term exams, written finals, take home finals, final projects, project presentations, project paper submissions, video presentation of the final project and for some courses all of the above! Of course all this was in addition to weekly or biweekly assignments. There was one thing though, I had to make one major adjustment and that was understanding that academia is different from the industry and it took me a good one year to make peace with the idea that assignments are not like requirements document of a product and therefore the questions could be (sometimes purposefully) vague (use your best judgement, I was often told), lecture notes could have errors (books have errata, so what?), semester projects could reach dead ends because the hypothesis did not hold and that was OK.
Work wise, I ploughed back in as much learning from the program as I could into starting new projects that could demonstrate business value, kept on demo’ing small proof of concept data science applications whenever I got a chance, and I think managed to create enough buzz around the topic which matched what was happening all around in the industry. All my projects at school were on work related topics that I could take back with me and this also meant that I did almost all my projects in a team of one. As the program entered its last phase I transitioned into a full time data scientist role in the same company as I thought I could provide unique value by marrying my domain expertise in telecommunications with my new found data science knowledge.
The time I spent at Georgetown was an incredibly rewarding experience. The joy of learning something new, the sense of accomplishment at being able to finish all the assignments and projects on time, the nervous energy before an exam, the sinking feeling that attending multiple holiday parties and preparing for two finals and projects and poster sessions all at the same time maybe isn’t such a good idea, and so many other bitter sweet experiences gave me memories of a lifetime. There is however, something much more profound that I realized while attending classes with 20 somethings and that is that a place of learning does not age, it feeds off the youthful energy of the students who study there, combined with their desire to seek new knowledge they keep the university forever young, forever full of life and it rubs on to you as well. Would I do it all over again, you bet.