Rejections are the evil part to face in Data Science interviews if I am not wrong to use the word 'evil' here, as nobody loves rejections. It is very difficult for the candidates who face rejection in their back to back Data Science interviews to stand positively in their next interviews and it often ends up in diversion of the career which you must not do. Diversion is the easiest option you could do after your first or many rejections in Data Science interviews but standing positively all over again and trying to crack the next interview in the next chance is the best thing you can ever do. The present article is written to deal with your Data Science interview rejection only.
Rejections are not dealt with over-thinking and negativity but by analyzing the mistakes committed and not to repeat them again.
The present article discusses the prominent mistakes that potential candidates make in Data Science interviews which lead to rejection and the ways how those could be corrected. Go through the article deeply and analyze the mistakes (if you are also not committing the same?). And the most important part-"Work on those mistakes". Learn from every rejection you face and build yourself stronger each day.
Beginners who are preparing for their First Data Science interview can also refer to this article as the article mentions some of the key mistakes that potential candidates make in their data science interviews which lead to rejections and advises how to correct the same. So beginners also have a golden opportunity to discover in advance of what mistakes they have to avoid in their interviews and how they need to move forward in clearing their first Data Science interview.
Mistakes that Potential Candidates make in Interviews and the ways to avoid those:
- Lack of research about the organization or of your position in the organization
Thorough research about the organization you are going to be interviewed is required from your end. Often candidates going for interview lacks on this part. They either do not know about the products or services that the company offers or do not have the necessary information about their particular role in the company. Every organization has some vision and some framework on which it works. Try to discover that. Also, the role of different positions in Data Sciences is different in different companies. Some organization uses a particular software while other organization uses another. So be mindful of that. In terms of programming languages, software, technical skills, roles, and responsibilities organization differ in various aspects. You should have the knowledge of your role in the company you are getting interviewed and all the necessary skills required by you.
- Know the products and services of the company
- Go thoroughly through the Job description.
- Research the company's website well.
- Know about the structure of the organization.
- Your resume might not present your qualities effectively
Indeed you have the requisite educational qualifications, technical skills, and business acumen but unless you do not mention it creatively on your Resume how would the recruiter would come to know? A resume is the first impression that the recruiter has of the candidate. Do not get loose on that part. Do not make your resume dull with the unnecessary information about your hobbies and interests but make your resume more effective by explaining about your technical skill, the projects you have handled and the challenges you overcame with your thinking and analytical skills in your previous company or your previous handled project. Give live examples of how your thinking and analytical skills prove well to your previous organization in terms of taking the right decision or how you used your thinking and reasoning skills in tackling a major problem in your projects.
The recruiter wants to see your achievements with proof examples. Do not mislead him. Describe your projects, your internship, and your relevant skills. Take help of an online Resume template in carving out the best resume for your interview.
- Impatient in listening to Recruiter's questions and jumping right to the answers
At the time of interviews, candidates become a little nervous about the selection process and with an aim to impress the recruiter they jump to direct answers without giving a thought about the questions asked by the recruiter. This gives a negative impression on the recruiter. The recruiter expects a relevant answer and is expecting the right approach from your end in answering questions. Listen to your recruiter patiently and think over the question for 2 minutes. The recruiter is not expecting a quick but a right answer. Be patient and think deeply.
- Lacking on practical skills
No theoretical knowledge of yours will prove good unless you possess the desired practical skills to solve the real world challenges. The job of a Data Scientist or a Data Analyst involves solving the real business problems which require excellent reasoning and thinking skills. In Data Science interviews the interviewers are always picking up a practical question for you, answer it well. Do not expect that only theoretical knowledge would be sufficient. Your practical knowledge in solving real-world challenges matters the most. Develop your problem-solving skills.
- Your confidence is shaking
I understand that you are giving your life's most important interview in your favorite company and the selection process matters a lot to you. But it does not imply that you are going to be fearful in your entire selection process. Fear is going to give you nothing but will only make you miserable. There is nothing to get fearful for when you possess the desired skills for the job. Some of the positions in Data Science like that of a Data Scientist require leadership and decision making skills and if the recruiter expects good confidence from your end than he is also not wrong. Improve on that part and Believe in your abilities. Show confidence.
There are a lot of online resources and a lot of books on Data Science available nowadays but still, a majority of the data science positions remains vacant every year due to the lack of knowledge possessed by the candidates. There are many questions that an interviewer puts before a candidate related to the basic concepts checking the conceptual knowledge of the candidate. Low understanding of basics could be a possible reason for your rejection when you were not able to answer one of the questions asked by the interviewer on the conceptual part. Do not repeat this mistake. And prepare your concepts well.
- Incompetent to use the latest software
If you desire to work in Data Science than a good knowledge of the latest software with a grip in programming languages like Python is required from your end. Some of the Data Science positions require knowledge of Hadoop, SQL, Spark, SAS, R, and many others. You should have the knowledge of the relevant software that you will be using while working in Data Science and must develop the skills for that.
And the last- Maintain positivity
For Candidates who faced rejection in their latest data science interview- Maintain positivity! this is not the end. Failures arrive only to give us the right direction to move ahead. They make us learn. Take it positively.
For a Novice: Cracking a Data Science interview is not too hard as well. Just the right approach with the right skill set is required.
I hope the above article helps you in providing the right direction to your career in Data Science. All the Best!