It's Data Science Jay here and today I want to talk about some common behavioral interview questions for data scientists and machine learning engineers and how we can answer them.

Mainly I want to go over frameworks and common strategies that we can use to solve the behavioral interview questions.

Also if you're new here, my name is Jay.

I'm a remote entrepreneur working in the data science space that used to be a data scientist.

Now I've founded a startup called Interview Query which is the premier data science interview platform.

So check it out but if you're new here please like and subscribe as well.

So for behavioral interviews there's a lot of videos out there about how to answer them and how to actually be good at them and those are all great but how do we actually communicate them specifically for data scientists and machine learning engineers or anyone else working in data.

Generally I see it kind of conform into three main buckets.

The first one is about how well you can actually communicate technical concepts when you're answering behavioral interview questions.

The second one is figuring out how to actually back up your resume and make sure that it holds up under questioning.

And then the third kind of behavioral interview question I see is more around culture fits and making sure that you're a good fit for the team.

So generally when people think about behavioral interview questions they kind of think about the recruiter screening right.

A recruiter will call you up, they'll ask you a little bit about yourself, some projects that you've done, your visa sponsorship, where you're located and stuff like that.

And so in general I don't think that's how most behavioral interview questions that actually matter but those aren't really the behavioral interview questions that matter.

More common than ever hiring managers and executives are actually the ones that are screening data scientists and engineers up front in the first one or two interviews.

The reason why that's happening is because a lot of the hiring managers and executives figure out it's basically a waste of time if you pass all the technical interviews but then on the last behavioral interview you fail because of the fact that you have to pass all the interviews to actually get the offer.

Because the executives have more judgment and more control over the hiring process many times they'll actually ask the behavioral interview questions at the very front to then reject you or move you on.

The key with demonstrating technical competency in the behavioral interview questions is to have two things.

One is strong technical skills and then the other is strong behavioral interview and communication skills.

You could have all the technical knowledge in the world but if you can't communicate it then it's basically useless to everyone else.

So a key thing to practice around technical communication is understanding exactly how to craft your stories for different kinds of interview questions.

Let's go over an example of when I was actually a hiring manager once for a prior company and asked an intern a behavioral interview question that they didn't really pass.

It was actually a pretty simple question.

I asked the data science intern candidate, tell me about a project that you're proud of and then the candidate responded with a lot of enthusiasm but they responded with an answer like this.

Yeah for my CS 100 class we made these robots and we programmed them using java and my robot won the competition and the TA said it was really good.

Okay so how did you end up building it?

We used java.

Okay but what actually made the robot good?

Well it won the competition.

You'd actually be surprised by how many of those answers that you get when you're actually hiring for data candidates and it's not that the project itself was bad it's just the fact that the candidate didn't go into any details that makes the project worth describing and as an interviewer everything we really care about is in the details of the project.

I mean obviously there's a fine line here I don't want you to get super granular telling me about the functions you use and the code base and stuff like that but generally it's about demonstrating that you actually did the project that you have the technical expertise to do it again and the fact that you use strategies and different kinds of frameworks to help you navigate through the project while working with other team members.

The best answers to behavioral interview questions like these are to frame it in a story and I know we have all these different kinds of frameworks like STAR to actually go through the situation, task, action, result and those are great to use but let's go through an example of how I'd actually answer this interview question if I was asked it.

Tell me about a time that you use data science to make an impact on the business.

That's a great question.

So at my company called Jobber basically it was a tinder style swiping app so you would swipe right to apply to jobs and you swipe left pass on jobs and my job as a data scientist was to improve the recommendation engine.

The existing model that was used in the recommendation engine before was this naive bayes trained model and it worked all right but I felt like there could have been improvements to it because of the fact that we were getting a lot of customer complaints about the model recommendations when they would upload their resume and they get a bunch of jobs that didn't actually match up to what they were looking for.

So instead I built an elastic search model that actually took a lot of the synonyms from their resume and then added it to the elastic search query parameter to return better jobs and also more jobs so they wouldn't actually run out of jobs from the previous model.

So the way that we actually tested this I didn't actually know if it was going to be better or not so we launched an A-B test we put only 10% of the users into the new elastic search model and then analyzed the total number of applications for both the control group and the experiment group.

Turns out that the new elastic model actually improved job recommendations and improved the amount of total applications by about 20% which in turn increased the amount of revenue that we made on the top line by around 10 to 15% because we get paid per application.

So quickly just to deconstruct that answer notice how in the very beginning I stated the exact problem that was going on.

We needed a new recommendation engine and that was my job to solve it.

Then I went into the details on exactly how I solved it by building this new elastic search query engine and at the very end I just detailed the results of what happened and how we launched it and what the business impact actually gave.

So it's not that hard it's more about crafting the story in a way in which you kind of define what the problem is and kind of explain and go through the details.

Notice how I did kind of go into details about the different query parameters we and how it was naive Bayes.

I dropped those as buzzwords because I know data science and I'm showcasing that I know data science.

But at the end of the day what matters is the fact that you can showcase that business impact you can talk it through and you can communicate that to someone else just like how you communicated that to your manager when you're first working on the project.

Adding structure to any sort of behavioral interview question answer is a huge win because one it just makes your story more engaging.

A lot of times I hear these stories from candidates and just honestly my mind goes elsewhere because you know I have better things to do.

I was hiring data scientists but if you make your actual story engaging if you actually try to add some structure to it your behavioral interview question answers will go a lot smoother.

A couple more behavioral interview questions are really common to data scientists.

I want to run through these really quickly.

Tell me about a time that you had to clean and organize a big data set.

How have you used data to elevate the experience of a customer or stakeholder.

Tell me about a time you had to communicate something technical to a non-technical person and then how would you communicate data driven insights to a business stakeholder.

Next up the second most important thing that I think happens in these behavioral interview questions is analyzing your resume and talking through your resume.

So a lot of time I'm pretty critical of someone's resume especially because of the way that anyone can kind of add anything to their resume without a lot of backing to it and so one of the key things I do as a data science hiring manager was is to basically go through and actually ask very specific key points about their resume that they listed below.

For example once I was asked to interview a candidate who said on their resume that they had increased the company baseline revenue by 30% month over month.

I was pretty skeptical about this but I kept on asking questions and turns out the business was an e-commerce business and the month over month change was from November to December which as you know is Christmas buying shopping season.

And so when I asked them what happened in the month of January suddenly they didn't have as much to say about their project influencing revenue.

So the core kind of learning here is really just to back up your resume and have a story and make sure that it's sound proof and tight for every single point that you put on your resume.

Generally I limit my resume bullet points to around four core bullet points for like the four biggest projects that I contributed to for every single job in my past career.

If you work in an analytics capacity where you're very business focused make sure you're not inflating a lot of those numbers or at least that you have a way to back exactly what you contributed to within analytics.

Similarly if you're in a machine learning role kind of focus make sure that you know exactly the technical details of the machine learning projects or the technical projects that you implemented.

If you put neural networks in your resume or something about PyTorch and I know a lot about PyTorch then I'm going to ask you a lot about it and expect that you have that same level of knowledge.

Generally if you can't explain your resume very well to an interviewer that's a huge red flag and probably one of the biggest red flags there are because then people think you might be lying about other things as well.

So in general just make sure that everything on your resume you can actually back up.

Lastly a lot of these behavioral interview questions for data scientists come around and culture fit is a huge issue for technical candidates because a lot of the times you get these really really strong technical candidates but that are really really bad at communication or working well with others and so generally for most of these culture fit questions they're testing you on a couple things.

One is straight up that you're not an asshole they want to know that you can work well with others.

If you're a senior candidate they want to know that you can actually mentor junior team members on the same team and then three if you're more of a junior candidate they want to know that you're eager to learn and you can demonstrate initiative for getting better in your role.

Most of the time culture fit is really hard to evaluate and there's not one size fit all in terms of how you can answer these questions.

The sad reality of it is that a lot of these companies have different cultural characteristics for how they run their companies and this is important for you as a candidate also to figure out because it might not be the same cultural fit that you're aligned with.

For example one company might really really enjoy cross-functional communication and also a lot of meetings but if you're the person who doesn't like meetings and really likes to focus on the code base or pumping out good code then it might not be a great cultural fit and you should also excuse yourself from the interview process then as well.

Probably the most common interview questions that come with this are more so around the situational kind of star framework type of questions where you can talk about situations where you demonstrated leadership or initiative to really make yourself seem like a really good cultural fit and team member.

So examples are tell me about a data project that you've worked on where you encountered a challenging problem, how did you respond, how have you gone above and beyond the call of duty, tell me about a time that you failed and what you learned from it, how did you handle meeting a tight deadline, tell me about a time when you resolved a conflict, provide an example of a goal you reached and tell me how you achieved it.

So in summary make sure that one you can communicate technical concepts and provide good stories and good structure for the projects you've worked on and also the basis for your technical proficiency.

Number two is to make sure to back up your resume and make sure it doesn't have any red flags on it and number three is just to overall assess culture fit in the best way you can demonstrate that you can show initiative and be a good team member with other people on the team.

Lastly I want to talk about today's sponsor which is Interview Query.

Interview Query is a data science interview platform that I am the founder of.

Basically we have hundreds of interview questions, courses, sequel editors, challenge assessments so you can assess your ability against other people.

It is probably the best technical data science interview platform out there and while we're about to launch behavioral interview questions on the site we haven't yet but if you're getting ready for a technical interview be sure to check us out.

Thanks for watching and I'll see you all later.

Bye.