It is so exciting to have trained over and over(Maybe done your ten thousand hours of mastery), honed your Data Science skill, and built your project portfolio to a point where you apply for a Big Data Science role and get accepted to have a Job interview.

With that said, only half the work of getting yourself the job has actually been done, so it will be wise to manage your excitement and get prepared to finish up strong. The rest of the work which lies in the impression you leave with your job interviewer can greatly impact the total outcome of your Job Application.

Little details like the kind of questions to ask to prove you are worthy of joining the team is very important.

Rise Networks continue to be involved with Data Science recruitment as such the things we look for are critical to having a sound Data scientist that meets the criteria. These things are irreplaceable and it is a secret we are willing to share with you! Hope the prospect of gaining insightful directions for a successful Job interview excites you? Without further ado, let’s delve in!

 

Below are the things we will be covering:

-Topics to expect in a Data Science interview.

-Data Science Job roles: What to expect.

-How to be ready for the Job interview.

-Common questions you will be asked.

-Questions you should ask

 

TOPICS TO EXPECT

The roles in Data Science are really broad but that should not scare you because there are essential aspects that your interviewer expects you to have background knowledge and experience about.

This is by no means an all-encompassing or comprehensive list but we guarantee you that these areas are always covered in this type of interviews

-Coding and Programming: Proficiency in any programming language usually give the interview the insight that you can easily learn a new programming language if need be but the two most required programming language for a Data scientist are Python and R.

-Statistics and Probability: Regardless of what speciality you handle in Data Science, Statistics and probability can not be avoided. These are core pillars and foundations on which Data Science is built as such you should make sure you are well versed in this aspect.

-Business Knowledge and application: This part is easily forgotten and negligible but it should not be so. Without proper knowledge of how businesses work, it will be difficult to under

Business intelligence or Dashboarding for instance.

-Data Modelling Techniques:

You will be asked about the different ways of modelling data, and it will be dependent on the scenario. Most importantly being able to say what method you will use and the reasons behind it is essential.

Data Science job roles: What to expect

Data Science is a field that undergoes loads of changes and improvement and is ever-evolving as such the roles are very broad. The key to scaling this aspect of your interview is making sure you keep the Job description you applied for very close to your heart and make sure it aligns with your technical ability and knowledge.

The list below can give you an idea of how wide the variation of job roles can be in Data Science.

  • Business Analyst
  • Data Scientist
  • Data Engineer
  • Data Visualization specialist
  • Data Science Project Manager
  • Statistician
  • Deep Learning engineer
  • Machine Learning engineer
  • Data Analyst

Making sure you prepare following the job description will ensure that time is managed well on things that are important and your preparation will be compact and well-focused.

 

How to be ready for your job interview:

These interviews can be a daunting task but the best way to surmount it is by preparing well.

If you have an Interview with Rise Networks this will also help and it won’t be illegal. It will only show that you have done your research well then maybe you can thank me later.

  1. Research the role: Read the job description properly, take notes of the soft and technical skills that will be required to carry out this role excellently then make your own research, while taking notes of best practices and noting down your recommendations that will show that you are willing to go the extra mile.
  2. Get an insight of what the interviewer wants: How do you achieve this by listening very attentively and asking questions. Note that some interviewers are looking for candidates with soft skills and good critical thinking so that they can quickly learn while some are looking for technical skills that will help the candidate swing into action when he starts. If you are given a scenario-based question ask as many questions as you wish to make sure you have a clear understanding of what your interviewer is talking about. Remember to brush up on past experiences because that can give you an upper hand in solving scenarios that might be thrown at you.
  3. Be honest about technical skills and software experience: It is best to put yourself in the best light possible but don’t agree blindly to every skill the worst thing that can happen to you is to quickly say you understand Regression but fail to explain Bayesian Regression or lasso regression. Most times the important part of an interview is checking if the candidate has character. Honesty will go a long way and shows a different kind of confidence and also proves to the interviewer that the things you say you know are true.
  4. Ask about the team you will be instructed to work with: It shows a desire to be a team member which is always going to be the case because Data Science project always involves the collaboration of experts.
  5. Do not shy away from discussing your salary: The best practice is to have a range in mind and it must have been researched properly so that you don’t price yourself out of consideration or underprice yourself.

 

Common question you will be asked:

It will be impossible to predict all the questions you will be asked, but it helps prepare your mind to go through some likely questions.

Below are some resources that will give you a clear direction.

Now that you have been given heads up, you can go and ace your interview and thank Rise Networks later.