Top data science teams around the world are doing incredible work on some of the most interesting datasets in the world. Google has more data on human interests than every 20th century researcher, while Uber seamlessly coordinates the itinerary and pricing of more than 1 million trips every day. With machine learning, and artificial intelligence, top data science teams are changing the way we ingest and process data, and they are coming up with actionable insights that impact the lives of millions. What if there were common patterns between the interviews top data science teams were giving that would let you master the data science interview process? What if the specific differences between various teams and their interview practices could be enumerated so that interviewing with a top data science team were more akin to a science than an art?
The technical interviews often used in hiring software engineers are a failure because they only test whether a candidate has performance anxiety, rather than whether they are good at coding. The interviews may also be used to exclude groups or favour specific job candidates, a study from North Carolina State University and Microsoft has found. "Technical interviews are feared and hated in the industry, and it turns out that these interview techniques may also be hurting the industry's ability to find and hire skilled software engineers," said Chris Parnin, an assistant professor of computer science at NC State and co-author of a paper on the work. The study suggests that a lot of well-qualified job candidates are being eliminated because they're not used to working on a whiteboard in front of an audience. Technical interviews for software developers are often based around giving a job candidate a coding problem to solve, then asking the candidate to write out their code on a whiteboard while explaining each step.
This is an installment of our ongoing tech job interview questions series. Today, we're talking about machine learning interview questions (with a few data science interview questions thrown in). If you're reading this, you've likely determined that picking up some tech skills is a savvy career move. But once you've learned them and updated your resume, how can you feel confident you'll ace the interview? This week's topic goes out to all the Python lovers out there.
IBM is a multinational technology company founded in 1911 and operates in over 170 countries worldwide. Today, IBM offers a wide spectrum of products and services that includes software solutions, hardware architecture (server and storage architecture), business and technology services, and global financing solutions. As a data driven-company, IBM understands the importance of data and data analytics at every layer of organization to drive better business decisions. Also, a leading provider of Analytics and Cloud-based solutions, IBM offers a full stack of cloud-based products and services spanning across data analytics, storage, AI, IoT, and blockchain. Check out this article about the Microsoft Data Scientist interview!
You're convinced that you want to enter into a data science career. You've done your research and even started to learn some of the skills needed. But how do you go from an data science enthusiast to a data scientist at your dream company? What does a data science interview look like? What do recruiters really think of your resume?