How to Deliver a Killer Presentation in Data Science Interviews

Very few people like giving presentations, but if you want to be a data scientist, you’re going to have to do it at some point, and actually being good at it can help you in your career.

Not only will you need to give presentations on the job as a data scientist, but you will also likely need to give a presentation to get a job in the first place. To help you do that, in this blog, we will explore 5 tips for giving presentations in interviews.

Focus on Your Impact

Projects tend to take a team. It takes a lot of people and collaboration to make them happen.

However, what other people did is not what interviewers are interested in. They want to know about you. Therefore, you should focus on your role and impact when discussing a project.

The best way to show impact clearly is to use measurable terms. As much as possible, use metrics and numbers that show the effect you had on the business.

It is not always easy or possible to connect your work to a business metric, but you still want to make it easy to understand. Talk about things like improving productivity or having something you developed adopted by multiple teams.

The point is that the interviewer should clearly see how your actions have a positive impact on the business. Avoid discussing things that do not clearly show how you helped the business. So, instead of trying to explain all the technical details of your project, you should focus on explaining how your actions impacted the overall business.

Use Your Best Stuff

What project you choose to talk about has a big impact on your presentation. Of course, you want to use your best stuff, but what exactly does that mean?

The best stuff does not refer to the project but rather to you. You want to discuss a project that will allow you to present yourself in the best light.

That typically means picking a project that had challenges and in which you were heavily involved. These two things will ensure that you demonstrate an ability to overcome obstacles and problem-solve as well as show that you have an understanding of the larger context and process.

List the Limitations

While it can be tempting, you should not pretend that your project is perfect. You should talk about what was not ideal about the project.

Listing limitations is actually good because it gives you an opportunity to talk about improvements. Showing that you understand limitations demonstrates a depth of knowledge and explaining how you dealt with limitations demonstrates problem-solving skills.

Think through The Technical Details

Once you’ve picked a project there’s one thing that I highly suggest you do to prepare for presenting: think through the technical details.

You should not expect to talk about all the technical details, but it is crucial to make sure you understand them so that you can answer any follow-up questions, such as Does it make sense to convert a continuous variable to a categorical variable? What if the distribution is long-tail?

Make sure you have thought through the project enough that you are comfortable answering such questions.

Behavior Does Matter

Besides having great content you also need to be aware of your own behavior while presenting to pull off a great presentation.

The goal is to walk a line between appearing confident and capable while also not seeming arrogant. Confidence shows that you can drive impact, but arrogance implies that you would not be a good coworker, which is why you want to find a balance.

To achieve this balance, it is important to show that you are a good listener as well as a good presenter. You can do this in a presentation by being open to questions and suggestions. Take the time to listen to questions carefully, and do not act dismissive of other ideas.

Conclusion

Overall, what all these tips point to is that a presentation is less about the project and more about you as a data scientist. This is not about explaining the facts of the project but rather about demonstrating your impact, problem-solving, knowledge, skills, professionalism, and ability to drive the conversation.

If you found this helpful and want even more, there’s a longer version of this post here.