The source came from the video below.
I was pretty inspired by her who is interviewee as a Uber data scientist.
Q: what are some exciting data science principles you're able to apply at work?
A: I think the most exciting part is when you run experiments, sometimes, you run into network effects. Sometimes, you run into cannibalization and you have to think of creative ways to address these problems while keeping your experiment results very robust. One example is say you want to run promotions on a set of bikes and scooters for a certain area and you want to see if that increases how many trips people are actually taking now by applying the promotion you take away more supply for from the control group if the treatment group actually do end up taking more bikes and scooters which might lower your conversion rate for your control group basically how do you make sure that your environmental controls are actually controlled.
Q: what do you not like as much about being a data scientist?
A: I think the most exciting part about being a data scientist is being able to derive insights and make decisions from incomplete information. Every day, you are making decisions and you never know if they're right or wrong and you never have complete information but data science actually quantifies that sort of uncertainty and I think it's a very empowering experience to be able to help business make decisions and move forward even when they have incomplete information.
Q: In a contrast, something that you might not like as much about being a data scientist?
A: I think one thing that all did a scientist hate the most is messy data that they have to clean up and somehow debug I encounter this in my day to day work and you have to write a lot of sequels and a lot that piece of the job is unavoidable, but I think it is also nice to always see that as an opportunity to improve the platform and infra it's good opportunity for people to say, "hey maybe I want to contribute a piece of open-source code to write something that could help make other people's lives easier or suggest something to your company to build internal tools or even build it yourself.
Q: tips and advice for those transitioning from formal education directly to industry?
A: I think it was very exciting and overwhelming at the same time. The things you learned in school are all printed on a textbook and a lot times they're outdated sort of old materials and in the workforce you realize that people are using new methods they're using experimental methods and people are constantly one top of new papers that are published especially in the world of data science where new algorithms are developed every year at conferences and I think one piece of advice I would have is to stay on top of news and stay informed go to conferences and meet people because data science is not of a super mature field and there is always opportunities for improvement. you should never be afraid to try new algorithms yourself or even try to develop a new method.
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