rtpstg673 Posted October 28, 2019 Share Posted October 28, 2019 Hi all, I'm currently applying to a mix of PhD and MS programs in bioinformatics as well as a few MS in statistics and would love an evaluation of my profile. Here's my background: Majored in neuroscience and completed the pre-med curriculum because thought I wanted to be a physician. After college, I decided to try something other than medicine before committing to that path, and I ended up at a startup whose core product is a matching algorithm. Quickly after joining, I realized I was more interested in data-focused work and population level impact than I was clinical medicine, so I doubled down on teaching myself python and took a few additional math and ML courses online since I had only completed calc I-III and intro stats in college. Pretty quickly I joined the (then) still-small data science team, and have continued to teach myself the relevant skills and math in the 3.5 years since I joined. For the past year, I've served as a data team lead (on the now ~15 person team) where I've juggled individual contributor work with people management, system design, long term planning etc. Ultimately, my intellectual interests and career goals lie in applying data science and ML to problems in healthcare. That said, I would like to buckle down on the math fundamental to the work I do everyday, so I can 1) be confident I'm using the correct tests and methods in the appropriate contexts and 2) so I can feel more comfortable reading and then implementing some of the more math-intensive papers in the field. Moreover, I've truly enjoyed the leadership aspect of my work (both the technical leadership and people management), and feel as though there's likely a ceiling in the leadership path in data science without some sort of advanced degree. So, by pursuing an advanced degree, I hope to flesh out my fundamental understanding of the math I use everyday and in the process gain a degree that will help prevent being boxed out from leadership positions in the future. Finally, for the PhD, I'm extremely interested in interpretable ML since the lack of interpretability is poised to be a major barrier to adoption of these methods in healthcare. Right now I'm leaning towards a masters (opportunity cost of the PhD is large), but if I were able to work with a group especially well aligned with my research interests in interpretable ML, I'd seriously consider a PhD. ---------- - School: Ivy, Major: Neuroscience (full pre-med curriculum) - GRE: About to take it, have been scoring 165-167Q on official practice tests. -GPA: 3.6 - Demographic: White male from the Northeast -Relevant coursework: only calc I-III, and non-calc-based biostatistics course. This lack of formal math courses is what I'm worried about. -Skills: python, SQL, a number of ML algorithms (SVM, logistic regression, various clustering, tree search, surrogate models), lots of visualization tools and packages, writing production code, writing tests, ETL (idk how relevant some of this stuff is to grad school), people management, project management - Work experience: 3.5 years in data science at a data-product centered startup. Was an early employee at the A round (<20 people), now ~150 people post series B. I've been a team lead on the data science team for 1 of those years with 3 (and growing) direct reports. -Academic research experience: 3 years undergrad. 4 posters at conferences, 1 published paper (3rd author, solid but not top tier journal). -Programs of interest: Stanford (Statistics and Bioinformatics), UW (Bioinformatics and Biostatistics), Harvard (Bioinformatics, Biostatistics, CS), UC Berkeley (Statistics). I'm looking to add some less competitive programs, so please send some suggestions! Link to comment Share on other sites More sharing options...
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