The title data scientist isn’t just handed on a platter to mere tech geeks – it is earned. From attending programming classes to tech fests to internships, being a professional in the sexiest job of the century is not an easy feat to achieve. You will agree that getting a career as a data scientist takes lots of applications, letters of intent, interviews, and exams – compiled with chances of being rejected at different institutions.
Most professionals in the field are constantly looking for new and better opportunities to increase their experience and add more zeros to their bank accounts. With so many qualified but limited slots, it is pretty challenging to land that dream job. IBM recently published a study that shows that another 28% of data scientists are needed worldwide to meet increased demand. However, most companies are bent on hiring professionals with lots of experience in significant positions.
Interviews are just as essential as your skill sets. Performing your best at the interview is paramount in your quest for the offer. You’ve spent months working on your portfolio and application, but you show up to the interview feeling anxious, and you end up messing up everything. With my three years of experience with conducting Data Science job interviews backed with research from other professionals in top companies, here are core reasons why most data scientists fail job interviews and how to avoid them.

Working silently
Do you like to remain quiet while working? It might have worked for you when alone, but it’s definitely not going to work while practicing in front of your potential employers. Most people don’t talk out loud when coding, and I get it. It’s a bit stressful. But you need to speak and buttress your steps for your interview. Most interviews are usually brief, and your interviewer wants to access your technical, progressive, and behavioral attributes in real-time. Hence, you’ll drastically drop points if you lack in any of these aspects.
When asked to solve a problem, repeat the question to get a clear understanding of the problem. By doing this, you give the interviewer a glimpse of empathy and accuracy in problem-solving. Actively interacting with the interviewer also buys you time to think and refresh your memory on techniques you need to solve the problem.
Here’s an example of a coordinated data analysis interview – displaying interaction while working:
"So if I got the question correctly, you want me to (repeat the question). Are we on the same page?
"Before I proceed, I access the figures and statistics I am working with to prevent any unnecessary errors as I progress.
"To begin, I launch my Jupyter notebook and import the dependencies, then run for errors. At the same time, preview the file location for the data. Is that acceptable?
"I have checked for null values by imputing data.isnull()
but I can see that it is running slow and filled with a couple of errors. Without wasting any time I will run a different command to see if I can boost the speed and efficiency as well…."
From this example, you can see how points are broken down, and the interviewer is carried along with every step as progress is made. This is just an example of technicality and interaction working in perfect harmony. The preferable and straightforward way to improve is to do many simulated interviews with your coach, clients, and peers – practice brews perfection. Feedbacks are critical; they are all you need to know what works best.
Ignoring the significant aspect of an interview during preparation
You’ve rigorously studied the fundamentals of programming, data science, and/or Machine Learning to ace an interview. You played your part and attempted the questions to your very best knowledge. If everything was so "perfect," why didn’t you land the job? I’ve seen lots of people lament that they were cheated or ripped off an offer because of their quality and professionalism. Don’t get me wrong, these guys are technical geniuses with a healthy portfolio, but they lacked essential skillsets. These skills determine how effective a data scientist will be while working under pressure, with a team, and the management.
Finding the right skills as a professional is comparable to playing Tetris set to invisible. Hence, apart from statistics and algorithms, there are positive skillsets you need to display to impress your audience.
67% of HR professionals said they’d withheld a job offer from a talented IT candidate because of a lack of soft skills, and here is why. Let’s face it; no one wants to employ a tech robot who doesn’t know how to appropriately interact with other professionals in the field. Data science isn’t just about numbers, as many think it is. Once your project is completed, you will need to present your work to your team or a superior. Therefore, once your audience spots a loophole in these basic but crucial skill sets, they slide your resume out of their desk – you lose the offer.
Most data scientists fail to develop and practice skills such as essential communication sprinkled with little humor, collaboration, interaction, selflessness, versatility, and reliance. These might not sound as important as technical tricks, but they are vital to your success. Before the day of the interview read books by professionals in or out of technology, centered on developing professional communication – Talk like TED: The 9 public speaking secrets by Carmine Gallo is a keeper. Explaining your solutions and observations from a project to yourself or preferably a colleague with mutual interests is also an excellent practice to improve your communication skills.
Failure to stay up to date with the latest trends in the industry
More industries are using data science to attend to their various complex business needs – extracting and analyzing data in split seconds are modern advancements all companies want to work with. With more involvements and automated technologies, data science as we know it is going through rapid developments; therefore, it is paramount to stay updated on changes from launch.
The internet of things (IoT), Machine learning, and statistics are just a few aspects of Data Science. Data is undoubtedly a broad niche and keeping up with the latest trends and changes is often challenging. Staying up to date is also tricky for me to adapt to – you’re not alone – it is overwhelming, and everyone, irrespective of knowledge, is struggling to keep up.
When preparing for an interview, you must stay abreast with the latest and trending information concerning the position you’re applying for and data science as a whole. The interviewer would be curious to get a fresh perspective on certain developments about the industry and other prospects.
To hasten your journey, here is a list of how data scientists can keep their selves updated:
- Attend Conferences: Conferences and technological gatherings are great ways to learn from other professionals and get opinions on current topics. If you need the latest tech developments broken down into tiny bits, start attending conferences.
- Twitter: Everyone has a smartphone these days; mobility of information is one of the benefits. Companies and top scientists in the industry use the microblogging platform to transfer information to a broader audience quickly. If you’re new, you’ll be surprised at the amount of juicy data science updates you’ve been missing out on. @GoogleAI, @OpenAI, @AndrewYNg, @KDNuggets, @Goodfellow_Ian, and others are my go-to sources for updates when I’m on the go.

- Newsletters: You might frown at this because of your "email privacy policy," but subscribing to a couple of newsletters is always a good idea. There are good newsletters from online blogs and publications you can hook up with. And If you’re like me, who is not a fan of daily newsletters, publications like Towards Data Science keep me informed weekly.
Final thoughts
Completing a certification or a four-year course will make you a data scientist, but not a competent one. Companies need applicants who can do more than just Programming. Careers in Data Science, Data engineering, DevOps Engineering, Artificial intelligence are based on tricky interviews. But the good news is they are easy to overcome with the right amount of practice, a positive attitude, and a drive to keep learning. If you’re preparing for your next interview or looking for ways to hone and master the craft, here are few tips that will guide you:
- Make prior research on the company and position you’re applying to.
- Interact. Interaction brews great relationships.
- Be precise and vast in your language. I think all data scientists must have advanced knowledge in Python, R, and SQL.
- During the interview, explain your models while practicing. Carry your audience along – both technical and non-technical.
- Most importantly, be honest. Don’t fill your application with techniques, languages, or tools you’re not thoroughly familiar with.