Interviews are used to exclude individuals who aren’t qualified for the position. They aren’t flawless, though, and some components of the interviews aren’t real-world work. Some people take a cynical approach to rejection and use this fact as an excuse.
It makes more sense for a corporation to reject good employees rather than hire terrible ones. We should all be aware of the tradeoffs between false positive and true positive rates as Data Scientists!
10 Tips for Acing Your Data Science Interview
In this article, I’ll provide you with some advice and tools for improving your interviewing abilities so you’re less likely to be rejected for the wrong reasons for a Data Science Job.
Prepare and practice the story beforehand.
You should anticipate questions aimed at determining your attitude/character, and you should have prepared several examples from your past that exhibit positive attributes. Amazon, for example, has a set of Leadership Principles that they interview candidates against. Find something similar and spend a couple of hours thinking about all the amazing things you’ve accomplished and writing down clear answers to these questions.
Practice solving problems
Problems can be solved by speaking out loud and writing the solution on paper. SQL questions are fairly common among Data Scientists. Look for sample questions on sites like JitBits or ProgrammerInterview and practice answering them. When you make a mistake, write down what you missed so you know where you need to spend more time.
Reduce the surprise
The most difficult aspect of an interview is having to think quickly to come up with answers when others are watching you and the clock is ticking. Even questions that you might believe are simple appear difficult in this setting. Reduce the likelihood of being surprised by understanding what to expect in each interview and preparing for all the things you know will happen.
Create a problem-solving framework and practice it
After conducting numerous technical interviews, I’ve discovered that the same structure can be applied to the majority of questions, providing me with clarity of thinking and forward momentum. I’ll write an entire post about the framework I use, but by working through many of these difficulties, you’ll design your own.
Know the Basics
The combinatorics sites on Brilliant.org will provide you with a review of key principles as well as examples to put yourself to the test. You should expect questions in probability and statistics, so don’t waste time trying to recall Bayes’ Theorem when you can prepare.
Understand basic technical ideas
You must understand bias and variance if you work in Data Science. You’ll need to know how to spot and deal with an overfit model. When the classes are significantly imbalanced, you’ll need to know how to cope with categorization issues. You must be aware of the advantages and disadvantages of various model structures.
Know the company
Consider the company’s goods, how your position affects the heart of the business and a few ideas for how you might execute your job to solve a major problem. I recall being interviewed for a position in Pricing Analytics and being asked, “How would you establish a price for this product?” I couldn’t come up with a good response, which is unacceptable. You should spend time thinking extensively about the issues that the role will most likely address and be prepared to articulate them with nuance.
Recognize that technical expertise is only one element of the equation.
People will also provide feedback based on whether or not they liked you. Project a positive attitude, be nice to them and their firm, and show your enthusiasm for the work while remaining humble about the chance. Someone is more willing to overlook a minor technical flaw in a friend than impolite behavior from a technically gifted individual. This also applies to your interactions with the recruiter.
Recognize that interviews are supposed to be difficult, and it’s fine to struggle.
The most essential thing is to keep going, even if you feel confined and unsure of how to go. Allow yourself to not become frustrated or contemptuous in public. You should state out loud what you’re suffering from and the difficulty you’ve uncovered in those moments. Explain why your solution isn’t working and try to figure out what element of it is causing the problem.
Finally, genuinely accept the concept that a job rejection is not a reflection of your abilities.
You’ll almost certainly be denied… numerous times. But that’s fine; this is a large globe, and we all know numbers are important. Even if they are technically proficient, not everyone is a good fit for every profession and team. You may be rejected for a variety of reasons, including a lack of a good fit, poor performance in an interview (even though you are qualified for the position), and reasons you will never understand.
Allow yourself time to process any negative emotions after being rejected, and then reach out to thank them and get input on how you can improve. If you need additional information or resources, check out Data Science Interview Questions and Answers.