How to Prepare for a Data Scientist Interview?

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Appearing for any sort of interviews could increase your adrenaline level. Cracking interviews need massive amounts of preparation and research. More so in the given scenario of appearing for a data scientists position, as only appropriate preparation and practice will get you cracking and performing well on the big day.

If you are an aspiring data scientist, you are expected to have a working knowledge, or understanding, or the capability to perform over multiple firms, with a bag full of skills.

Continue reading to understand a quick step-by-step approach on specific areas of Aptitude, Technical know-how, and skill sets required to not only clear the interview but to also excel in the field of Big Data and Machine Learning.

The thing about data science is that its application, and hence expectation across industries varies to a large degree. The role is interpreted differently across companies, some might call a PhD Statistician as a data scientist, to others, it means proficiency in excel, while to some it may mean a generalist in Artificial intelligence and Machine Learning.

  • Step #1: read the Job Profile, specifically for Skills, Tools, and Techniques. If the job description is not self-explanatory or in detail, then some research on the company is non-negotiable. Be clear as to what type of a data scientist position you are applying for. The interview is usually a combination of an Aptitude Analysis, Technical Knowledge, Attitude Analysis. Most organizations in recent times test the applicant on fundamental topics to gauge their fit in the company, attributes like Language Comprehension, Analytical Reasoning, Quantitative Aptitude, etc…, can be easily cleared by reading up on the same to brush your skills.
  • Step #2 –Brush up on important and relevant concepts like these before the interview. To test your technical understanding on the subject, most probably there will either be a technical round or an assessment, case study, which will essentially gauge your knowledge in statistics, programming, machine learning etc…, ensure you are fluent in relevant languages like R, Python, SQL, Scala and Tableau.
  • Step #3: will be to brush up on elementary topics like….
  • Probability – Random variables, Bayes Theorem, the Probability distribution
  • Statistical Models – Algorithms, Linear Regression, Non- Parametric Models, Time Series 
  • Machine Learning, Neural Networks.

So here, essentially you will be tested by the medium of a case study or discussion, on your problem-solving capabilities. It will help if you are able to define the problem for them on the presented scenario, and link the same to the suggested solution and its impact on business. While doing so, cite examples of case studies, or research papers for supporting the suggested solution.

  • Step #4: while you may come with the required skill sets and qualities, ensure through-out the interview you show the willingness to learn and flexibility in adapting to the current organisation, as Data Science and its applications are unique.
  • Step #5: to have a tight resume and pre-empt on ways you will link your experience with the given position during the course of the interview.
  • Step #6: is to carry out data science projects specifically if you are a fresher, there are many public domains available for the same. In addition, it is also advisable to take up MOOCs – Massive Open Online Courses to gain exposure to different as well as focused applications.

Remember, in recent times the role of a data scientist is viewed as someone who can bridge the gap between multiple features of a business. So it is not expected or required of you to be a specialist in all the aspects, but you should be able to link the features, idea and provide solutions across domains. To stand apart in an interview you should not only show your individual strength and domain expertise but come across as a person with enough management skills, along with good communication and technical skills who can blend and get to the crux of a problem.

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