This week I talked to Taher who recently joined Apple as a data scientist. I asked him about his last interview loop, what he did to prepare for his interview, and where he sees data science going in the future. You can find Taher on Twitter @taherelsheikh and Medium.

Interested in learning about the difference between data science at FAANG companies and startups? Check out this article on "Data Science at a Startup vs FAANG Company"!

Hello! What's your background, and how did you get into data science?

Hi, my name is Taher Elsheikh, and I just joined Apple as a data scientist focusing on Apple Music. Before that, I worked at Intuit as a data scientist in the Payroll team and the In-Product discovery team. Before that, I was in Washington D.C finishing my M.S. in Financial Mathematics while working as a data scientist in a consulting startup called Red Oak Strategic.

I knew I was interested in a lot of different domains like computer science, statics, product management, analytics, etc. so becoming a data scientist seemed like an excellent fit for me.

Working at Intuit was great and I learned a lot. The data science team there is embedded in the product, so the role is more of a technical product management role where you work with product managers and engineers daily to create new features, debug existing metrics and run a lot of experiments. Ultimately though, I wanted to move to a more consumer-facing company and work on products that I personally use, which is why I made the switch to Apple.

How was your overall interviewing experience when looking for a new job?

This time when I was interviewing, I was considerate about getting a lot of interviews and making sure to schedule the ones I wanted most at the end. Last time when I interviewed for data science positions, I got the offer from Intuit, and I just took it. So this time, I wanted to broaden my horizons a bit more, and I ended up getting around 6 to 7 interviews.

My data science interviews were tough because the interviews were completely different from one another. Data science is very unstructured, and it's so hard to ace interviews because you can't prepare 100% for each one. The data science skillset is extensive. I feel like a lot of people say you have to interview at a lot of companies before you get the hang of it, but it didn't feel like that way since each interview was just completely different from the one before. For example, at Facebook, the interview was focused on product sense questions and statistics, but at Google, the interview was more around machine learning and consulting type of questions.

What helped prepare you for the interviews?

The SQL questions in Interview Query were super helpful. You never find SQL questions dedicated to data scientists. Usually, Leetcode or Hackerrank have SQL questions that are targeted for software engineers. So finding SQL questions that were more focused on analytics and data processing for data scientists was very helpful.

The product questions on Interview Query were also helpful for many interviews. Given how sporadic and variable a lot of the product questions are, it was beneficial to get a sense of how to think about structuring your answers when formulating metrics and diving into problems.

Also, recruiters would send me a lot of material to prepare for upcoming on-sites. Although some of the content might be helpful, there's a lot of times when the material was misleading, and I ended up wasting a lot of valuable time. This happened with LinkedIn and Facebook.


Where do you think data science is going in the future?

In my view, machine learning is getting less and less relevant to be honest. In data science job settings, companies want data analysts and technical product managers more than they want core data scientists who build models. There are more openings at Intuit, for example, for analytics positions than there are for machine learning ones. I think it has to do with the fact that companies need people that can communicate and strategize and can move the product forward.

Thanks for reading