By Catherine Adenle
With the rapid adoption of technology in every sector, data science has critical applications across industries – healthcare, manufacturing, government, biotech, finance, retail, engineering, telecom, transportation, etc.
The use of Machine Learning (ML), Artificial Intelligence (AI), Augmented Reality (AR), etc., data and semantic extraction skills that will make products, processes, systems and operations more seamless, precise, accessible and affordable to millions of people have made a data science career more desirable.
The importance of businesses of every sector gathering data, structuring data and utilizing data effectively extends much further. For these reasons, a career in data science has a staying power in the tech job marketplace. It provides opportunities for professionals who study in a related data science field to make valuable contributions to organizations and societies.
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The data scientist role has been named the number one job in the U.S. by Glassdoor, and according to IBM, there is a predicted increase in the data science job openings and demands for related roles for years to come. The U.S. Bureau of Labor Statistics also reports that the need for data science skills will drive a 27.9% rise in employment in the field through 2026.
However, not only is there a huge demand for the role, but there is also a shortage of qualified data scientists. So, to break into this high-paying, in-demand data science role, the know-how skills, advanced education or knowledge of data work is generally required.
As reported by KDnuggets, “Data Scientists are highly educated–88% have at least a master’s degree and 46% have PhDs–and while there are notable exceptions, a solid educational background is usually required to develop the depth of knowledge necessary to be a data scientist.”
Data Science is a massive group of disciplines:
And multiple data operations in:
See 16 analytic disciplines compared to data science
Due to this multitude of disciplines and operational use, there are opportunities for you to join in. But first, you must apply for the role in any organization of your choice and ace the interview.
Interviews can be daunting. You want to be confident in your knowledge and skills and show the interviewer that you are the right fit for the job.
Here are a few things you can do to succeed at your data science interviews.
Data Scientist is a blanket term, and there are many roles in the data science ecosystem; when you are applying for a data science job, research the requirements of the specific role. All the roles differ one way or the other, and hence, interview techniques will be different, too. You must understand the differences between Data Science roles and the one that you are applying for in particular and understand the job description thoroughly.
Also, it is good practice to understand the kind of business you are applying to. Before you can ask the larger-picture questions, ensure you have a competent understanding of everything your position entails.
Then, researching the employer is the best way to become a stand-out candidate during the hiring process. Put on your detective hat and investigate for details about the potential employer, any tech projects and capabilities. This will better prepare you for the interview and position you as the best candidate.
What do they do? What is their mission? Dig around and know the technology the company is using, what they are working on, and how they contribute to society.
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Revise your resume several times a day before going for the interview. Filter out what you can do without, and mention the most applicable projects and skills required for the role.
This one goes without saying: don’t mention anything on your resume that you can’t back. You should be able to explain the projects and skills you mention thoroughly. Whatever you reference on your resume can be the topic of the questions they ask you.
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If an interviewer asks you about a project you listed and you cannot answer his queries satisfactorily, it will be a significant indicator of your shortcomings. In the worst scenario, recruiters might even flag your application as one not to invite for an interview in future.
It is a fact that more than 80% of recruiters will first check a candidate’s LinkedIn profile before scheduling an interview. Show the value of your digital presence. We are in the digital age, which means a piece of paper with your resume on it will only take you so far.
Keep your LinkedIn Profile current, share data science posts, engage in tech discussions and showcase your previous technical accomplishments. You can also post your code on GitHub and further build your portfolio.
Share your thoughts on technical issues in blogs and articles, invite feedback and talk to industry folks; recruiters find such practices add to your credibility and integrity.
Data science recruiters love puzzles. It’s highly likely during the interview process that you’d be asked to solve puzzles, patterns, number sequences, and intelligence questions, etc. Recruiters habitually use these to determine how you would deal with data analysis and algorithm structures. Practice puzzles beforehand to keep your brain sharp. It’s a great way to show your analytical knowledge and how you approach a problem.
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Recruiters ask for definitions and what you understand by specific data science terms and concepts. If such questions have previously confused you, you should prepare before the interview. Freshen up on the data science-related terminology by quickly reviewing the relevant glossaries and reading up on the concepts.
A few concepts and terms to review might be:
At times, the interviewer will ask you to solve a case study or a particular assignment. These can be of two types, and they give you a take-home assignment, which, as the name suggests, is something you can do at your discretion at home and take your time. Another and most common is an on-site case study problem, which can take 3-6 hours, depending on the type of problem.
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Don’t get overwhelmed by such steps; take it in your stride. Think of it as a perfect way to demonstrate your skills. Go the extra mile with your solution and deliver to impress the recruiters.
If you keep these things in consideration, there is no doubt that you’ll be able to ace your data science interview.
Be confident; you’ve got this.
Want to add to the 6 sure-fire ways to ace your data science interview? Leave your comments below.
