I work as a tech lead at a startup called Metrim Data. Metrim Data is a startup that tries to understand the internet and provide value to businesses through the insights it gains. Typically, my work involves building several data pipelines mining data and trends from Twitter, Instagram and Twitch. Frequently, my work also involves building and testing various machine learning and deep learning pipelines to suit our clients’ business and marketing needs. Overall, it’s a pretty fun gig where I try to acquire data and play around with
How I got involved: Metrim Data was incubated under AlchemyX Startup Generator (link: https://alchemyx.io/). I came across one of their fliers during and found their work pretty interesting. I set up an interview and was glad to be accepted due to a couple of Kaggle projects that I was involved in the past. I met some very talented people in all fields and never left.
Additional Experiences: I’d say looking at other people’s code on Github is a great exercise. DSC 10 and 20 just scrape the surface of what can be done in Python. There is a great many things you could do with python, for example, you can detect faces with 6 lines of Python code (link: https://github.com/ageitgey/face_recognition). Recently, I built a reusable Tinder bot in Python that caught a lot of attention among UCSD students, thanks to the UCSD meme page. It’s exciting what you could do with Python and Data Science, so I’d definitely recommend other students to build small projects and put it up for others to see on GitHub as I have first-hand seen recruiters skim through the candidates’ Github profiles to make a decision about their application.
Useful Curriculum/ Online Courses: I found concepts that I learned in DSC 30 to be pretty useful. I recall moments where I was faced with pretty stiff engineering challenges and the concepts of graphs and hash tables that I learned in DSC 30 were the ideal solutions. I’d also recommend Coursera and Udemy as great online resources to pick up some machine learning skills. I have certainly learned a lot through them.
Additional Information: Current Students: I would definitely recommend participating heavily in Kaggle competitions. In my opinion they are a great way to pick up some good data science skills and meeting other experienced data scientists. Besides, the competitions are very exciting and would really help you build an affection for data science as a career. Also, potential employers constantly lurk around looking for good Kagglers. It seems like a no-brainer for any data science student. Also, AlchemyX is always looking for talented candidates, feel free to apply through the application on the website.
Current Faculty: I would love to be involved in a project that would help the current data science faculty conduct their operations in a better manner. I’m bad at coming up with examples, but imagine something like a python script that speeds up the grading for TAs by 10% by automatically checking for style, et cetera. If a faculty thinks that there’s something that would help them administer the class in a more efficient manner, I would love to help.