November 7, 2022

Tips for Acing Your Data Science Interview.

Last month, we partnered with DataQuest on a webinar sharing insights on how to ace a data science interview and land your dream job. Two of our co-founders, Eunice Baguma Ball and Oliver Angelil, shared an overview of the current realities of the data science job market, what to look out for in a typical data scientist interview process and tips on how to impress your potential employer.

Below is a summary of the key points discussed. You can also watch a recording of the webinar here.

Current Realities of The Job Market.

The Data science job market is growing exponentially and is expected to reach 11 million job openings by 2026 (US Bureau of Labor Statistics). The market has grown both in terms of interest from talent and demand from companies and shows no signs of slowing down. Getting information on how to navigate the process of entering the field and finding the right opportunities is crucial to your ultimate success.

The Typical Data Science Interview Process.

The typical data science interview usually starts with a coding test. This is fundamental at the beginning in order to quickly assess the competencies of the candidate. Candidates who pass will then typically be asked to submit their transcripts and GitHub profiles for further review.

Questions on the coding tests are usually geared towards trying to understand the way you think and the techniques you use to solve the coding problems. When working on the coding test, always strive to express your own thinking and creativity in order to impress your potential employer. Creative problem-solving is quite important in your data science journey. It is a key skill that is a core requirement that can propel you into success in your data science career.

What are Interviewers Looking For?

During the technical interview, your interviewer will be interested in knowing why you chose to solve the challenge the way you did, other previous data-related projects you may have worked on, and the tools and programming languages you have used before. You may also be asked detailed questions on data science terms and concepts so it is good to update your knowledge on these before the interview.

The interviewer would also want to assess your approach and the techniques you would use to solve the problems presented. While there are very traditional and fundamental ways to solve data-related problems, always take the opportunity to show your creative problem-solving skills in order to stand out.

Working on data science projects always requires a collaborative effort among team members. The interviewer would look out for candidates who have the willingness to share and try new ideas and communicate well within the team in order to get the project completed. Having these traits will surely make you stand out, impress the interviewer and potentially get one step closer to getting the job.

Practical Experience is Key.

Real-world data projects are key to the hiring process. If you do not have past professional experience, working on open source projects is a great way to produce some tangible results that showcase your actual skills and competencies. Fellowship programmes like Ishango.ai and courses on Dataquest are also great ways to get access to practical opportunities to sharpen your skills.

Coding is essential to Data Science so make sure to polish up your coding skills. Do not underestimate the importance of your GitHub profile; contributing to open source projects provides evidence of your coding competencies to potential employers.

Be Honest and Ask Questions.

Honesty and authenticity are critical to standing out as a candidate. Don’t lie about things you do not know or they’ll come back to haunt you. Be confident enough to admit the things you don’t know and demonstrate how you are trying to get better at them.

Come prepared with questions to ask your interviewer. Make sure these questions are tailored to the organisation and the role you are applying for as this shows you have taken time to do your research.

Data science can also mean different things within companies so titles may not have the fixed meaning that one would expect. During your interview, be sure to ask what the job actually entails and get more details on the job description in order to understand what will be expected from you in the role.