Preparing for the analytics interview

This post covers groundwork and tactics to get the rest of you to follow your foot in the door.

This is the second of two posts to help you land the analytics job you want. Do check out the first about resume creation here.

Just as your resume is a golden document and not just something you churn out like the average to-do item, preparing for the interview involves more than showing up and winging it with your smarts. Even though you work day-in, day-out in your current analytics job, you need to put in some hard yards before the interview given that any or all of the below are possible:

In short, it’s quite likely that you’ve got so stuck in your job routine that you’re either out of touch with or altogether lack the skills required to nail a given interview.

Failing an interview is not an indictment of your competence, nor is professional competence all that is required to clear one. Nailing an interview requires a combination of preparedness, sharpness and presentation to go along with what you know.

Do note — unless you’re interviewing for an entry-level role, you can be sure that companies are hunting for candidates who are ready, or can be groomed into the role. However, the extent of required grooming that is tolerated is business context and company culture. It’s non-negotiable that you be good at core skills — data pull and manipulation, statistical rigor and presentation.

Every company’s analytics hiring process is composed of different rounds. They test candidates on the below 5 themes (not necessarily one theme per round, or one round per theme):

We will now cover each of these in detail.

Industry knowledge

You are not doomed if you don’t have exposure to the prospective employer’s industry, but there will be time prior to the interview that you can use for basic research about the industry and company. It would help you to be prepared with the answers to the below questions:

There is no interview round dedicated solely to this, but you will look pretty good if you relate the questions you are asked back to this homework. At the very least, it’ll help you avoid wasting precious brainpower in the problem solving rounds, and give you ammo to respond to some of the fitment questions (more on this later).

It is not necessary that any or every question you face in data handling or problem solving will be industry-specific, but if it is, doing this will give you a leg up.

Logical reasoning

Several companies want to identify candidates who display logical thinking and numerical aptitude before they invest time in deeper discussions. This could be done through a phone screening or aptitude test, where the candidate can expect questions of broadly three categories:

There are tons of these questions available online to help you prepare, and you’ve definitely spent several years tackling questions like these if you went through central board exams, national engineering entrances and national/international postgrad entrance exams in India.

Data handling

Different companies do this differently — you have evaluation methods ranging from online programming tests (with ample anti-fraud measures that use your webcam) to face-to-face white boarding. The skills tested would be some or all of:

Companies generally will not hire candidates who do not demonstrate competence in all of these — Data storage, data pull and data wrangling are analytics bread and butter.

The interviewer can casually ask you short programming questions (“What’s the difference between having and where in SQL?”, “What are some ways of processing a dataset in R that are faster than a loop?”) before getting into the problems to code/whiteboard, so be sure to brush up your fundamentals and practice.

There is, of course, a chance that you’ve never worked on the exact tools that the employer is using, but you can just inform them about this, and explain your methodology on a platform you both know about, or do so theoretically. Different employers and roles require varying degrees of programming expertise — The more senior the role, the less negotiable it is that you be good at Python/R and SQL as a bare minimum.

The biggest let-down is when the interviewer goes ahead and quizzes you on a platform listed in your resume only to hear you to respond with “Well, I only did an online course on this”, or “I’m still learning it”. That sort of thing does not belong on your resume, then. Resist the temptation to put it in there or state that you know it.

Business problem solving

Any analytics project goes through the below stages:

Business problem -> Analytical problem -> Analytical solution -> Business recommendations

This section of the interview is usually designed to see how competent you are at every step of this journey. This might take place over one or multiple rounds.

To begin with, a good analyst should be able to turn a broadly stated business context into specific analytical problems, specifying the target metric(s) and possible influencing factors along with hypotheses around how those factors would impact the target metric(s).

For example, the context provided could be “How do I optimize retail stock?”, which breaks down into:

Analytical problems with target metrics: Maximize on-shelf availability, minimize wastage/dormant stock

Factors: Product rate of sale, Promotions, Seasonality, store size, store type etc.

After this, the interviewer could continue to quiz you on how you would see this through to an analytical solution (or pivot to a different context for this — a problem statement from your resume, for example). This is a non-exhaustive list of questions that can give you an idea of what you can expect:

Intimate knowledge of a technique is important to clear these hurdles. Please ensure you are familiar with all these details for projects on your CV, or state clearly the extent of your involvement in said project if you are not familiar (I once saw a candidate fall to pieces when a problem context was taken straight out of his CV, with a slight modification. That really shouldn’t have happened).

To polish this section off, you might be asked to turn your output into business recommendations. Coefficients and p-values might be your language of love, but business stakeholders speak English, and it is in English that you must communicate your results to them.


This round can seem open-ended and a lot of candidates complain about the subjectivity of it, but most employers are looking for the same things, which candidates struggle to broadcast given the opportunity. Things such as:

To this end, there are several questions you could be prepared for heading-in. Many of these are commonly asked regardless of industry or role:

I can’t give you a script here, your answers will be your own. I will list down some guidelines, however:

Final notes

There are a couple of other questions you should be prepared for.

Please feel free to leave a comment with any questions or feedback.

Good luck to you in your job search!

Football. Anime. Manga. Tennis. Words.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store