Making (LinkedIn) Connections: Alumni Spotlight on Xia Hong

Xia was a Fellow in our Summer 2015 cohort who landed a job at LinkedIn.

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

I am an experimental physicist in soft condensed matter by training in my PhD program at Emory University. There are three things that I think have helped me a lot to become a good data scientist:
1). The solid background in physics and math that I obtained back in my college. The knowledge itself isn’t necessarily reflected in my day to day work now. However, the training of logical thinking and critical thinking is really beneficial in a long run.
2). Persistence in finding root causes. The massive amount of data can easily leave you feeling swamped. I believe that always asking why until you get to the true cause of the problem is really essential. Sometimes, the insights are hidden behind and need our motivation to dig them out. No matter if it’s driven by natural stubbornness or original curiosity, I find the persistence usually a great help for walking the last mile to the final discovery.
3). Passion for solving problems using data. There is a joint program in our department where I took computer science courses for a masters degree. In the course projects, I started to find my passion in solving practical problems using data science approaches. Now I am working on product analytics and I cannot imagine how tough it could be without that passion and curiosity about what we can do to improve it.

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

Completing mini projects on diverse and up-to-date topics really helped me to be confident about how to apply my technical skills to solve problems in practical situations. The hands-on experience from end to end, especially the relevance of the techniques to that in industry, is going to be a long term benefit for me and certainly for any previous and current fellow.

In addition, the opportunity to have conversations and build relationship with different companies. This is not only for landing a job, but more for a healthy business relationship in a long term. Getting the benefit from the bridge built up by The Data Incubator between fellows and partners is one thing. Another important goal of networking is for future communication and collaborations. I kept in touch with some of the fellows after we finished the program and we keep each other posted. It is invaluable having fellows experience the transition from academia to industry together, sharing thoughts and helping each other.

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

Learn some basics about the programming languages like Python and SQL if you haven’t used them before. The program is intensive. Given finite time for finishing mini projects, it would be difficult if you need to figure out how to use the language syntax and basic functions.
Read The Data Incubator blog about everything to get a good sense about some highlights on data science technical skills, industrial demand, and any high level requirements for data scientists. I guess you are reading them right now!
Particularly, being an experimental physicist, I found two things that are important to keep in mind. (1) Approaching the problem from a more practical perspective. The problem you are going to solve is probably not a fundamental science research question. And, usually, there’s a specific goal related to the business in industry. Of course, not all data scientist roles are the same. Some of them are doing research in data science, which would be another story. (2) Communication is more important than I thought. Massaging on data sometimes makes us focusing on details and talking in technical terms. Explaining the results, insights, and solutions in a non-scientific language is really crucial for delivering your findings and further having impact on the final problem solving.

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

Many of the tools that I learned were new to me. I enjoyed learning pandas, using the machine learning package in python, iPython notebook, AWS, Hadoop, etc. There are too many of them.

Personally I’m happy with the lesson that I learned about asking for help. I was used to thinking through a problem in depth and solving problems independently. This might be OK working in a lab. But most of the industrial environment is collaborative. Learning how to solve problems together is helpful for career development especially at the early stage. I was not doing a good job asking people questions while I was in the program. I felt that I should know or learn the skills by myself since I’d been selected to be a fellow. But asking questions of other fellows helps with more than just solving the problem. It’s about optimizing time usage, communicating, initiating new ideas and helping each other.

And lastly, tell us about your new job!

I’m going to be working at Linkedin on the business analytics team. More specifically I will work on product analytics. Mainly the goal is using data analytics on products to provide insights to the business. There is a big amount of work tightly related to the business questions, which is the part that I enjoy a lot. What I also enjoyed is the great balanced culture at Linkedin with its rich data, smart people, and real data-driven decision making.

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