Solving Interdisciplinary Problems with Data Science: Alumni Spotlight on Wendy Ni

Wendy was a Fellow in our Winter 2017 cohort who landed a job with one of our hiring partners, Facebook.

Tell us about your background. How did it set you up to be a great Data Scientist?

I have a PhD in Electrical Engineering from Stanford University, where I’m currently a postdoc. My doctoral and postdoctoral research focus on the translation of novel magnetic resonance imaging (MRI) technologies to clinical neuroimaging applications, and the extraction of “hidden” imaging biomarkers from conventional clinical images. In my research, I utilized my engineering, programming, study design, and communication skills to solve interdisciplinary problems with real-world impact. I am now pivoting to data science, because I want to use my quantitative and analytical skills to discover hidden insights and guide decision-making for immediate applications in industry.

What do you think you got out of The Data Incubator?

The Data Incubator has introduced me to many toolboxes and techniques, as well as soft skills and information related to the job search. In a nutshell, it revealed my “unknown unknowns” and provided the resources for me to convert them into “known knowns”. I also really enjoyed learning from and collaborating with fellows, scholars and instructions.

What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?

I would advise a TDI applicant with my background or other interdisciplinary research backgrounds to think very carefully about what would make them happy and effective in their career, and try to understand what kind of positions and companies would be most appropriate. This can significantly influence the way they manage their time during TDI, and the way they approach the job search and interview process.

What is your favorite thing you learned at The Data Incubator?

I really enjoyed learning MapReduce and Spark/Scala. I really appreciate having a gentle, guided introduction to the awesome tool of distributed computing.

How did you come up with the idea for the project?

I am very interested in using open data to understand and improve service provision by local governments. I initially wanted to use my dataset to predict case processing times, but I realized that being able to anticipate the number of new requests is even more useful for helping cities proactively plan resources and meet resident needs. I changed the focus of my project and created SFNeeds.

What technologies did you use and what skills did you learn at TDI that you applied to the project?

I used a range of tools including Python (including pandas, scikit-learn and flask), Leaflet, Tableau, Bootstrap and Heroku in my project. My exposure to the various Python tools and packages has been invaluable for this project.

Describe the business application for this project (how could a company use your work or your data)

The goal of this project is to empower poorly performing taxi drivers to increase their hourly wage. Our algorithm can be used to tell a poorly performing taxi driver where they should go to search for customers in order to maximize their hourly wage. You can easily build smartphone apps to deliver these actionable insights directly to taxi drivers, and this could potentially have a lot of impact.

And lastly, tell us about your new job!

I am going to be a data scientist in analytics at Facebook. In this role, I will be collaborating with engineers and product managers to create and improve Facebook products. I look forward to helping my team use data to drive decision making in a rigorous and evidence-based manner.

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