Best Data Scientist Interview Questions For An Offline Video Interview

Data science is one of the fastest-growing careers and uses multiple methods of extracting unstructured and structured data to draw relevant conclusions. Data science is closely related to data mining, machine learning and big data. Hiring employees for this role is not easy for businesses. The business needs employees with the right technical and soft skills to fulfil all responsibilities.

Interviewing for data science positions can be time taking. Since recruiters and hiring managers have multiple prospects to interview, the process can be even more time taking. Also, assessing the applicant’s technical skills in the earliest stage of the interview process saves time.

And understanding technical knowledge via phone is not the best alternative. In this case, Offline video interviews or asynchronous interviews are the best bet.

This feature is especially beneficial when companies are looking to hire candidates for technical roles, such as data scientists. Recruiters get to watch interviews multiple times and can analyse the applicant’s verbal and non-verbal clues better.

Asking The Right Data scientist interview questions

The two aspects of conducting an offline video interview are- questions asked and the tools used.
These two factors will help you create a seamless and meaningful interview process. The last thing you want is a bad-quality video or a candidate answering questions irrelevant to the job. It will wash off the importance and purpose of organising these asynchronous interviews. Therefore it is better to focus on both.

While planning the questions, ensure you cover all the important data scientist interview questions you wish to ask the prospect. To make it easier, divide your interview into two segments- first, with general questions, followed up with technical questions.

Segment One: The General Questions

Soft skills are the new power skills. The general questions help you assess the candidate’s soft skills such as communication and analytical skills. The questions will also help you understand their expectations of the role. While drafting general questions, begin with the basic questions like strengths and weaknesses and gradually level them up to their responsibilities. It is also better to avoid getting too technical too soon.

Once the introductory segment is completed, you can add questions like:

01- What are your strengths and weaknesses?

It helps to understand the candidates’ self-confidence and how well they carry/ present themselves.

02- Where do you see yourself in 5 years?

It is another common question, but it helps to understand a candidate’s determination and long-term goals. It will also give you an insight into whether their plans and ambition align with what the company offers.

03- Why do you want to join our organisation as a data scientist?

The purpose is to understand the candidate’s motivation to apply for a position in your company. An ideal answer should reveal their inspiration for the role and the company.

04- How do you overcome professional barriers?

Again, a question that gives a deeper insight into a candidate’s problem-solving and critical thinking skills. To make their answer more viable, you can directly ask the candidate to demonstrate any real-life experience.

05- How have your previous roles or experiences shaped you for the role of a data scientist?

It will give you an insight into the tasks they handled during their previous job and the professional experience they will bring along. An ideal answer should include insights about their day-to-day activities in their previous company.

Segment two- Time to Talk Technical

Once you set the number of warm-up questions, you can start with the more technical data scientist interview questions.

01- What tools and devices do you plan to use as a data scientist?

The purpose is to understand how well the prospect knows about the tools and what languages they understand.

02- How do you keep the selection bias in check?

Selection bias is the inability to extract random samples of data. Data scientists are required to pick random data to make their insights effective. This data scientist interview question will entice candidates to show their knowledge and personal opinion on the subject.

03- How do you organise big sets of data?

Deriving relevant conclusions from a large set of unorganised data is the primary responsibility of a data scientist. Hence, this becomes an important data scientist interview question. It will give you an insight into the automation tools and methods they use and how they use them.

04- Is a large amount of data always preferable? Explain your point of view.

The data scientist interview question helps determine the candidate’s philosophy and general thought process regarding data. An ideal answer will be where the candidate discusses how a preferable amount of data depends upon situations and concludes with situations where different data would be applicable.

You can add more technical questions relating to their KPAs during the job. A thing to remember while setting these data scientist interview questions is to set your expectations beforehand about what type of answers are the deal breaker. It will make your evaluation process easy.

Use FlexC For A Seamless Offline Interview Process

Offline video interviews are one of the in-built features of FlexC’s AI-powered marketplace. The in-built features also help recruiters with technical assessment.

When the pre-screening process is complete, employers can conduct offline video interviews, assess the interviews with team collaboration and onboard the candidate.

The set of features includes:

  • Technical assessment of bulk candidates, saving time and effort.
  • Adequate instructions and support for a seamless experience.
  • Customisable questions for each interview
  • Collaborate with multiple interviewers and contribute to the hiring process irrespective of geographical locations.

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