Hearing “No” at a job interview can be quite a demotivating experience. Applying for a data science job involves a lot of stages, while the interview stage can be crucial to gauge if an applicant will fit the job role and the company culture.

Often, applicants get rejected for mistakes they commit that can easily be avoided. Here are a few mistakes you should avoid while sitting for a data science-related job interview:

CV filled with buzzwords

There is a huge hype around using words like deep learning, machine learning, and neural networks in a CV while applying for data science roles. An applicant should mention such skills in their CVs or during the interview only if one has worked on real-life projects previously in a job or internship. Saying that one knows these skills despite having no practical experience will only increase their chances of rejection. An experienced hiring manager can easily detect if an applicant actually possesses the knowledge and skills they claim to have.

Lack of a good portfolio

A good portfolio of all the data science and analytics projects the applicant has pursued gives the hiring manager immediate proof of the applicant’s capabilities. In addition, an updated GitHub profile highlighting all the work you have done and how it has helped clients – can act as a great source for potential employers.

Not the right experience

Professionals from other fields like IT, consulting, or even marketing often show their previous experience in data science roles and how they have already used algorithms and analytics in their previous job roles – which might backfire in the interview if one cannot back up the claims. If the applicant does not have deep knowledge on a particular topic (that one has claimed they have) as expected from a data scientist applying for the specific job role, they may get rejected by the hiring manager.

Not focusing on the job description properly

All data scientist jobs do not require the same qualifications and skillsets. The job profile and KPIs vary from company to company. Some may require the applicant to have advanced degrees like PhDs in statistics, mathematics, computer science, or machine learning, and deep learning skill sets. Blindly applying for jobs that say “data scientist” in the job description will serve no purpose. Instead, the applicant should go through what the job actually wants them to do and how the skills one possesses can be aligned to the job profile.

Lack of application of analytics to business problems

The ultimate objective of a data scientist is to solve a business problem through analysing data and building algorithms. A hiring manager will give the applicant a business scenario and question how to solve the problem. The manager is not expecting the applicant to develop the entire solution themselves, especially when the applicant has only a few years of experience. 

The manager is trying to see how the applicant approaches the business problem and whether they can perform what is expected of them when put in such a setting. A candidate should ask more and more questions to the hiring manager to figure out the business problem entirely and then suggest how their skills can help solve it. If simpler concepts of data science can be applied to solve the problem, they should suggest that instead of using buzzwords and overcomplicating the issue at hand.