Even in 2018, the data science title continues to reign on, emphasizes Linda Burtch of Burtch Works. However, while the popularity of the term data scientist is soaring, and companies continue to pour substantial amount of money in building their data science and analytics team, the job descriptions usually vary. In this article, we will talk about how to crack an in-person panel interview for an entry-level data scientist. There are a few questions in a data science interview that could be a deal-breaker and could bag you the coveted job. And for some aspiring data scientists – few job descriptions can be extremely overwhelming with postings that read more like a data science glossary.

And though the manpower crunch for data science expertise continues to grow — market demand continues to rise. According to Ness Digital Engineering CTO Moshe Kranc, as a new generation of college graduates emerge for whom Big Data has become a standard part of software education, the technology changes so quickly that no one can have a complete know-how of everything listed in a job description. John Foreman, Vice President of Product Management at MailChimp mentioned in a post that it is tough to find and hire the right people in data science since the field is so multi-disciplinary and given how data scientists are treated as the new Renaissance people, it can be a tall order for a person to achieve all the skills.

Let’s list down the questions for the in-person interview for an entry-level data scientist

An entry-level data scientist would be someone who has up to four years of experience working as a Business Analyst, earned his/her stripes in SQL, Python & data visualization skills and wants to move to this Data Scientist level. The entry-level role also applies to a Senior Software Engineer looking for opportunities to work on analytics and machine learning projects.

Before we dive into the potential interview questions, let’s lay down the resume guidelines:         

• It is best to keep the resume to one page and under the name, list down email, contact number, Github, and LinkedIn account details
• List down the recent work experience with details about the programming languages/ technology used (such as Python Notebook) and business outcome (such as increased revenue by x percentage, achieved this result)
• List down key projects you worked on which will also highlight your skills around data organization, manipulation & databases among others
• List down all the certifications and bootcamps/hackathons you participated as this shows you are dedicated to learning
• Lastly, mention your educational background
• Mention your active interests
• Make sure you mention a bunch of keywords such as Automation, analytics, machine learning, python, SQL, NoSQL, MS-SQL to make your resume searchable
• Set up alerts on job sites for titles such as Junior Data Scientist, Senior Data Analyst, Junior Machine Learning, Senior Analytics Consultant
• Here are a couple of resources to refer to: Data Science Resume Guide & DS Interview.

Let’s dive into the in-person interview

Now that you are the initial phone screeners and exercises, you have to brace yourself for the in-person panel data science interview. We are breaking down the questions role-wise applicants are most likely to be asked (remember this is just to help crack a data science interview.

Possible Questions by Vice President, Data Science

• So, data, tell us about it…
• How did you do the exercise? Did you experience any issues with it?
• Let’s talk about a project from your previous experience? (The aim is to understand your ability to handle projects end-to-end)
• What as a process you made more efficient at work?
• So, you basically took a process that didn’t work well and productionized it?

• Can you tell us about your way of explaining findings to people?

• What’s that one thing you want to improve?

Possible Questions by Chief Technology Officer

• Do you have any entrepreneurial experience that you want to talk about?
• What are the areas you think you need more development in?

• Why did you leave your last company and why this company?
• What has been some of the key learnings from your previous stint (Data Science is still an evolving field and all prior experience is appreciated)
• What was your proudest moment so far in the Data Science journey?

Possible Questions by Data Scientist

This could perhaps go in a more casual, laid-back way with discussion around past projects that helped the business grow, work challenges faced during previous stint. The discussion would also possibly entail  details about the current data science team structure, projects they are involved in and how the right projects are prioritized.

• Can you cite one example which you thought was completely outside the box and helped in turning around the project?
• What is logistic regression?

• Can you walk me through how to develop A/B test?

• Can you tell me about you got into Machine Learning and the learning process?

• What is the largest amount of data you worked with and details about the result?

Possible Questions by VP Marketing

In here, the questions will be more business centric, for example how can one go about determining the right number of recommendations to show on the website/app or perhaps related to A/B test.

• Why do you want to work with us?
• In your previous role, how much interaction did you haves with the marketing/sales team?  

• How good are you at communicating your insights visually?
• Are there any data science activities you are involved in besides the day job?

Questions You Should Pose

• • (To the CTO/Data Science Head) Can you tell me the projects I will get to work on if I land the job?
  • (To the HR) I hope you got a chance to know more and my skillset well. If you have any more questions or any reservations about my qualifications, I hope we can discuss and address that.
  • Lastly, don’t forget to negotiate about the paycheck and don’t sell yourself short, since not everybody can tick-off all the boxes in the JD.