Hiring at Google are on some particular basis, so here is a brief introduction to what Google looks for while hiring a Data Scientist. Through these tips, you can easily prepare yourself with the interview for a data scientist.
Talented professionals can easily come across their profile on Google. As it is not easy to find people having good passion and talent. So this article will give you an overview of how you can easily get a job at Google as a Data Scientist.
Everyone knows that entering into Google is quite a difficult task. Hiring bar is set and is really high, but this post will give you guidance on what you can do to prepare.
Know your stats
Math like linear algebra and calculus are more or less expected of anyone who is hired as a data scientist, and we look for people who live and breathe probability and statistics. Promising candidates will have the equivalent of at least 3 or 4 courses in probability, statistics, or machine learning anything beyond that is the icing on the cake. You should be able to ace the homework and exams in your probability and stats courses many of our data scientists have actually taught these courses before coming to Google. There are a few sites out there, such as stats.stackexchange.com, on which you can find some great questions and discussions to develop your statistical skills.
Anything less than that could be supplemented with courses in technical fields such as computer science, economics, or engineering. Original research can also help.
Get real-world experience
Demonstrate that you have had experience working on real-world data. Coming up with a new regression estimator for a few UCI datasets is nice, but those datasets are often used for comparing methods, not for getting real-world experience. We really want to see something that demonstrates that you have had a chance to get your hands dirty on real data, and lots of it. This means you have spent time collecting your own data, cleaning it, sanity-checking it, and making use of it.
Write a script to pull data from one of Googles public APIs and write a blog post about what you have found. Use a web scraper to scrape a few hundred thousand web pages and fit some topic models to create a news recommendation engine. Write an app for your phone that tracks your usage and analyze that. Be creative!
Spend time coding
We don't expect all our data scientists to be hardcore engineers, but we make sure everyone we hire is capable of coding. The best way to demonstrate this is to know how to code ahead of time. Increasingly, our applicants point us to GitHub for examples of their coding skills. Well typically expect that you have already become familiar with scripting languages like Python and SQL and one or more numerical languages like R, Julia, Matlab, or Mathematica. Bonus points for knowing a compiled language like C++ or Java. If you would like to learn more coding, check out Khan Academy or other coding resources.
The easiest way to achieve the above criteria is to be passionate about some data science problem! Perhaps you have spent a few years studying some problem for which data provides a natural solution. Perhaps you've written code to interface with public APIs, from Google or otherwise. Ideally, you're passionate not just about the methodology used to frame the problem, but also the problem itself.
Note that you have multiple options
At Google, data scientists may be hired on one of several job ladders. If your talent skews toward the engineering side, you may want to pursue the standard software engineer track and ask for a more analytical role if it skews towards numbers, you may want to pursue the quantitative analyst track. In a post, later on, we might outline some of the differences between the two tracks within Google Engineering.