Data Science job interviews are beyond checking for technical acumen.

It is now a well-established fact that data science jobs are on an exponential rise. With companies trying to analyze data to gain valuable insights, understand trends and more, data science roles, like data scientists, data engineers, data analysts, analytics specialists, consultants, insights analysts, and more are in high demand than ever. No wonder that Harvard Business Review has named it as the sexiest job of the 21st Century in October 2012. However, preparing for a data science job position can be intimidating.

While it is often suggested that the key to crack such an interview is having technical preparation about technology and possessing technological aptitude. However, Ted Kwartler, VP of Trusted AI at DataRobot and Harvard Adjunct Professor, shares from his real-life experiences that "anticipating audience needs is the most important factor at each interview stage. In reality, data science candidates often over-index on technical acumen, and neglect the fact that every evaluator is reviewing different attributes. This implies that one doesn't need to go overboard on technical acumen rather than analyze that every evaluator reviews different attributes.

Here are a few pointers that can help interested applicants keep in mind before the data science interview.

Reading the Job Profile

The hiring managers generally want to know if the candidate is actively interested in what the company does and that the person has already begun thinking about how she could bring value to the company in that role. They want to see if the applicant's skill set matches the job's requirements. Hence, it is important to read the Job Profile, especially for skills, tools and techniques. If the job description is not self-explanatory or in detail, then it is helpful to research on the company. One must be clear as to what type of a data scientist position she is applying for. Review the different nuances expected in various job openings and prepare accordingly.

Build a Digital Presence

While it is true that one will find numerous available resources online to help prepare for the interview, he should also have a social media or relevant digital presence. Recruiters will admit that they often check a candidate's LinkedIn profile before calling them for an interview. So, one should get started by having:

• LinkedIn Account that is tailored (updated) according to job skills mentioned in the job listing. It won't be helpful if the job applied is for a data scientist position while the profile portrays you as a wildlife photographer.

• GitHub Account. Nothing is convincing like a well-documented work. Therefore, applicants can put their coding works and projects to GitHub so that the recruiter can see their work first-hand, before calling for interview or tests.

• Answering data science-related questions on Quora, blog writing to showcase one's understanding of the subject matter. Even registering and participating in data science community events can display the applicant's keen interest in this niche.

It is important that while building a LinkedIn account, treat it like a digital resume. One must be thorough while explaining about work experience, data science projects. For instance,

'Helped with XYZ project' won't be as impactful as 'Helped with XYZ project by improving accuracy by 23% and thus generating 45% increase in revenue'

Studying and Reviewing

It always pays to practice. One must have a careful reviewing of one's data science projects, work on puzzles to improve problem-solving skills, study about use cases of data science job roles, stay alert on current data science trends, have a clear understanding of the difference between confusing terms or concepts (e.g. Precision and Recall, False Positive Rate and True Negative Rate, etc.)

Brush up on fundamental subjects topics like

• Probability – Random variables, Bayes Theorem, the Probability distribution

• Statistical Models – Algorithms, Linear Regression, Non- Parametric Models, Time Series

• Machine Learning, Neural Networks

One should also practice how she can use the STAR (situation, task, action, and result) method to answer these questions, which will basically be used to evaluate

1. Teamwork/ culture fit,

2. Communication skills,

• Problem-solving,

1. Presenting convincing actionable insights

2. Versatility

Apart from that, pay attention to body language.

It is also beneficial if the applicants can study about the engineering/DS materials from applied companies. Further, learn how to whiteboard to make sure of not being caught unaware when asked to pen ideas.