.png){kind=logo}
Data science is the most in-demand job globally and has a great future scope.
Data scientist jobs are assumed to grow by 36% in the next decade.
Interview preparation goes far beyond an evaluation made merely on technical skills.
Data science is one of the most demanding career options today, and the requirement for data scientists is expected to grow at an incredible rate in the future. According to the report published by the US Bureau of Labor Statistics, data scientist jobs are expected to rise by 36% in the next 10 years.
Considering the increasing demand, Harvard Business Review has labelled data scientist as the “sexiest job of the 21st century.” However, as a beginner or an employed person looking for a career change, it can be difficult to navigate this field. Through this guide, readers will learn about the ins and outs of data science interview preparation.
Data science involves studying data to extract meaningful insights for businesses to take action. Scientists are required to examine large datasets using various principles and practices from mathematics, artificial intelligence, and computer engineering. The derived data insights are then used to guide businesses in decision-making and planning strategies.
A typical data science project will have the following main processes: data ingestion, storage and processing, data analysis, and communication of the data that influences business decisions.
Also Read: What is Data Science?
Expertise in Python and statistics is not enough to crack a data science job interview. It requires a deep understanding of the company's core values, its competitors, and more. Creating a tailored portfolio for the company can also help the process.
Here are some to-dos listed below that can make an applicant stand out from the crowd:
Aspirants should visit the company website and social media profile to collect as much information as possible about its major products and services. Furthermore, they should consider how those offerings can be improved and present their thoughts when the opportunity arises.
Studying the major competitors and finding out how the interested company stands apart from others can help analyze the strengths and areas for improvement. This understanding can be a great advantage to the applicant.
Trying to understand the cultural values adopted by the company can help new joiners align their aspirations with the firm. This information can be found easily on websites. Most companies have separate pages that share their values and work culture details.
Applicants can visit the interviewer's social media profile (for instance, LinkedIn) if their names are disclosed, and see what they have recently shared on the platform to get additional information about the company.
Also Read: Top Data Science Jobs in X, Facebook, and LinkedIn
A typical data science interview consists of four rounds: a telephone interview, a virtual interview, an in-person interview, and an HR round. Below are some common and important questions asked in these four rounds.
The purpose of this interview is to check whether the candidate is suitable for the role. These are some of the general questions asked in this interview.
A quick overview of the past qualifications and experience.
Fundamental questions based on machine learning, statistics, artificial intelligence, or SQL.
A virtual interview is like a technical round, where the interviewer will test the problem-solving skills.
Assignments based on coding tasks are provided.
Questions about machine learning algorithms and real-world case studies are sometimes asked.
Interviewers can also ask the applicant to describe their past projects.
Based on company policy, this interview might require the candidate to be present at the specified location, or it might also take place online.
Problem-solving and whiteboard coding assignments.
May involve discussions with data engineers and product managers.
The HR round is mainly to assess the personality of the candidate.
Questions like “Why are you interested in this job profile?” and “What are your career goals?” might be asked.
“How do you handle feedback?” can be asked to judge whether the candidate is a good team player.
Salary expectations, notice period, and company policies are also discussed in this round.
Overall, data science is a rapidly growing career option that promises a great future. With the increasing advancements in technology and the amount of data generated every day, data science jobs are in huge demand. Now is just the right time to get started.
