It’s not always easy to stand out during any interview process. As you’ve probably heard, the need for Data Scientists is growing rapidly. However, so is the population of available talent out there applying for these competitive positions. The reality is that in a pool of qualified applicants, you need every edge you can get.

This post is designed to help you achieve that edge by laying out a multi-step system to product knowledge and ideation that I’ve used time and time again with great success.

1. Get to Know the Product

This may seem obvious, but a surprisingly small amount of people do this well. As Data Scientists, it’s common practice to focus on solidifying technical skills during interview preparation. This is justified, as without a technical foundation in place, you probably won’t be moving forward.

With this being said, I would argue that knowing the product well can be just as important when it comes to leaving an impression on your interviewer.

By getting to know the product inside and out, you’ll be able to see things that other applicants couldn’t. This especially comes into play during common product-thinking or problem solving assessments throughout interviews.

“In Data Science, if you want to help individuals, be empathetic and ask questions; that way, you can begin to understand their journey, too” — Damian Mingle

Another unseen benefit is getting to explore your interest level for the product or company. At the end of the day, you probably don’t want to spend 40+ hours weekly on something that you aren’t passionate about or at least somewhat fascinated by.

My recommendation is to take the time before an interview to really get to know the product, ideally from a user’s perspective. Literally block off some time each day leading up to the interview to use the product if possible.

This may mean one less coding exercise that day, but it will undoubtedly pay itself off in the long run as you develop the domain knowledge needed to speak knowledgeably about projects and ideas regarding the company and their offerings. This brings me to step number two.

2. Generate Ideas

Once you’ve used the product a good bit, take the time to brainstorm ways to make it better. This can be any number of things including ideas, features, or data-driven projects that you may be interested in working on.

Think about what the future looks like and where the next steps are for the particular team. Think about what you would like to work on and what you could bring to the table in order to make the companies offerings superior in some way, shape, or form.

“Creativity is the process of having original ideas that have value. It is a process; it’s not random” — Ken Robinson

For more clarity, I thought I’d share what my ideation-focused notes looked like for an actual interview with LinkedIn. Note that I took these points word-for-word from my notebook, so they might be a bit hard to interpret.

  • Job common app, fill out one application and send it out
  • Recommendations for ideas within the post prompt to encourage more interaction in feed
  • Improved job recommendation model — seems redundant
  • Career Advice Tips feature for optimizing profile for views and engagement
  • Profile or resume A/B testing feature for the user — Company x looks at employees with these skills/background
  • Job Comparison tool — currently in place, but no side by side view
  • Better way to engage content sharing — maybe 1% of users post 90% of the content?
  • Better way to keep track of jobs you’ve applied to within the platform — make the spreadsheet obsolete

As you probably noticed, these are a bit all over the place. Not all of these ideas are good ones. Most of them aren’t particularly original, and an even larger proportion of them aren’t very practical.

Nonetheless, ideas like these are still priceless. Often times, long-time employees will struggle to think of things that may come easily to the fresh eyes of a new candidate. At the very least, it provides the company with insights as to how their users think about the product and how it could be improved. So, now that I have ideas, what’s next?

3. Sell Your Idea

Okay, not really “sell”, but rather bring up interesting ideas and projects that you came up with during your brainstorming sessions.

You don’t need to force this either. The opportunity will usually present itself when the interviewer asks you ‘What kind of things would you like to work on?’ or when it’s time to chat and ask a few questions at the end of the interview.

At the very least, the employer will be impressed with your research and your passion for the companies mission and product. At the best, they will find your idea interesting and remember that when it comes time for your assessment. A third scenario is the case where the company is already pursuing your idea in some way. This may even be more impactful, since it shows that you are on the same page with their vision moving forward.

Quick Review

Let’s recap. We went over a system for interview preparation regarding product use and ideation. For me, this takes the form of scheduling time to mindfully use the product while taking notes on things that are interesting. I’ll then take some time to materialize these notes into business ideas, features, or projects that I think would be cool or beneficial before bringing them up during the interview

Note that this process may look different for you, and that’s perfectly okay. The point of this exercise is to develop the domain knowledge needed to excel at your interview. Every additional step in that direction is another step towards making a lasting impression and mastering the data science interview.

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