United States Bureau of Labour Statistics predicts that there will be 11.5 million data science jobs by 2026. The World Economic Forum predicts that by the end of 2020, data scientists and analysts will become one of the top emerging job roles across the world. The hype and glamour surrounding the data science industry are only increasing. Today, everyone wishes to crack data science interviews and begin their career as Data Scientists. But data science is a multidisciplinary field, and acing a data science interview is not an easy task. Data science interviews can be pretty overwhelming, no matter if you are an experienced professional or a beginner. It is okay to not know everything and it is acceptable if you make a couple of mistakes.  The right data science interview preparation is what makes all the difference. It’s a lot easier to crack a data science interview when you know what to expect and how to prepare. Data science careers are still evolving and the job requirements and responsibilities vary from industry to industry, company to company, and role to role. If you’ve never attended a data science interview before or are preparing for your next data science interview, follow these tips and steps to get yourself ready. 

Data Science Interview Preparation – Getting Started

The stories you hear from other job seekers can make data science interviews feel more intimidating and daunting. So, it’s better to be prepared before facing the interviews. These are just some of the questions you’ll have in mind:

And we have the answers to these questions that will help you big time to crack the data science interview with your maximum potential.

1. Preparing Your Data Science resume

Crafting the perfect resume is just half a battle won while preparing for any interview. This is especially true for Data Science interviews. Your data science resume should be crisp and concise – no recruiter wants to spend hours reading through your CV. The relevant data science skills should be highlighted. Create customised resumes for different data science job roles. Also, keep in mind the company you are applying to; the expectations of a start-up firm vis-a-vis your experience will differ greatly from that of an established firm. Find out how to build a good data science resume.

2. Applying for Data Science Jobs

For many, this would seem like an unimportant step. After all, your resume is ready, your skills have been brushed up; all that remains is to upload your resume on any of the job portals, find a relevant data science job, and apply. This strategy has sadly gone out of date. If you opt for this method, your resume will end up among a hundred similar ones flooding the recruiter’s inbox. And, you need to get past the recruiter, to come under the hiring manager’s radar. This is the digital era, so make use of your online data science portfolio and contacts to apply for jobs. The recruiter must notice your profile over others in order to shortlist you for the interview.

Once your data science resume is customised for various data scientist job applications, it’s time you begin to think about the next step in the job application process: the data science interview process.

Congratulations! You’ve landed a data science job interview. NOW, WHAT?!

The Data science interview process varies from company to company, project to project, and also from job role to job role making it difficult to prepare for. However, over the last decade, some kind of structure has evolved for the data science interview process. These steps serve as a comprehensive guide for aspiring data science professionals to understand the structure of the data science interview process.

Disclaimer: This is a typical structure of a data science interview but it might not be necessary that every data science job interview that you appear for will fall into this structure. Every company approaches its data science interviews differently. The interview process varies based on the company you are being interviewed for and the data science job role because each company has its own hiring policy. 

Data Science Interview Structure – The Different Interview Rounds You Will Encounter

Data Science Interview Round #1: The Preliminary Telephonic Screening

Good News! The company liked your data science resume and wants to discuss more about data science jobs. Once you have been shortlisted for an interview, most recruiters contact you via a phone call for the first round of interview. This interview round is meant to assess if a candidate is qualified and enthusiastic enough about data science to proceed to the next level. Perform well enough in the preliminary telephonic screening round to land an opportunity for an on-site or in-person interview. Usually, a telephonic interview lasts somewhere between 30 minutes to an hour depending on whether the interviewer is a recruiter or a hiring manager from the data science team. 

Remember, that this will be your first interaction with your prospective employer and must be treated with due seriousness. A casual attitude may give the wrong impression. Depending on the data science job description, you can keep the answers to a few common questions ready before the interview, but refrain from simply reading the answers off your screen. Make sure that you are in a quiet room while attending the call, with no distractions. This is also the time to ask a few questions, to find out more about the company/job role, information that will not be available from other sources.

The phone screening interview typically begins with a conversation where you get to know each other followed by interview questions that will cover your professional and educational background, including the projects you’ve worked on, the company, and the data science job role you are applying for. Depending on the company, the telephonic screening might sometimes be a little extensive with a couple of technical questions added if the hiring manager wants to evaluate your technical abilities before scheduling the next interview round.

Pro Tips to Ace the Telephonic Screening Data Science Interview Round

  • Prepare a one-minute elevator pitch for the “Tell me About Yourself “ question.
  • Show enthusiasm about the company and the data science projects the company is working on to showcase your passion and curiosity for the data science job.
  • If you are being interviewed by a recruiter  (with no technical knowledge) play up your soft skills but if you are being interviewed by the hiring manager of the data science team who is a techie by heart, then show off your tech knowledge to come across as a good fit for their team.

Data Science Interview Round #2: Technical Round or the Programming Round

The second interview round is often a series of technical or programming questions asked by people currently in various data science job roles who could be your future teammates or team leads. The complexity of the programming round depends on a candidate’s background. This round often involves testing a candidate’s basic programming skills in Python or R or SQL or any other programming language that you might have listed on your resume. But this again depends on the candidate’s background, if the candidate is a Ph.D. in Math or Statistics or if he/she hails from a mechanical engineering background, they might not get asked questions on SQL. But, if he/she is from a computer science background or from IT, they can expect SQL questions in this round, something like –

You have two tables namely Publisher_Info (fields in the table Publisher_Id, Video_Id, and Video_Duration) and Consumption_Info (fields in the table User_Id, Video_Id, and TimeSpent_Watching).  

  • How many publishers have at least one user who viewed their videos?


FROM Publsiher_Info x

INNER JOIN Consumption_Info y

ON x.Video_Id = y.Video_Id

  • How many minutes of video does an average publisher have?

SELECT SUM (Video_Duration)/ COUNT (DISTINCT Publisher_Id)

FROM Publisher_Info

Questions in the technical round can vary in complexity from a simple SQL query to problems requiring dynamic programming based on the experience level. The data science interview questions in the programming round at a service-based company are likely to be much easier than at a product based company. For instance, when being interviewed for a service-based company you might be asked simple questions like – “Given two sorted arrays, how will you merge them into a single array ?” or something like “Find the median of two sorted arrays.”.  In the technical data science interview round, the candidate is required to solve a problem by writing lines of code but that’s not just it. The hiring manager wants to see how you think, act, and code – so it’s okay to make mistakes but how you communicate, the approach you follow and result is more important. You could have one or two rounds of programming interviews but that again depends on the company and the team that you are interviewing for.

Pro Tips to Ace the Technical/Programming Data Science Interview Round

  • Hiring managers usually let you choose the programming language you’re most comfortable with and ask questions around the same. One thing to do before you land at the technical interview is to specialise in at least one data science programming language, preferably Python, R,  SQL, or Java. 
  • Irrespective of the difficulty of the questions asked, always clarify any doubts you may have before you start writing the code.
  • Always take 2 to 5 minutes to understand the problem and expectations before you start solving it.
  • Follow a brute force approach when answering a programming question and make sure you let your interviewer know that you are first trying to find a solution to the problem in a non-optimal way before you can think about writing an optimised solution to the same problem.

Data Science Interview Round #3: Real-World Problem Solving or a Coding Challenge 

The next key round in the data science interview process is the coding challenge or the real-world problem-solving case study commonly referred to as the “Assignment Round”.  From the smallest of start-ups to the major tech giants, everyone has at least one real-world problem-solving interview round. This could be a take-home coding challenge with a deadline of a few days or an in-person interaction integrated with a real-world case study with a timeline of 3 to 8 hours. Depending on the company, you can expect multiple rounds of coding interviews. A candidate might learn all the theory under the sun but if he/she cannot solve a real-world problem then they will not be deemed a perfect fit for the data science job. This is an opportunity to impress your recruiter; aspiring data scientists should go the extra mile and not just stick with meeting expectations.

Springboard mentors recommend getting hands-on experience working on diverse data science projects with the help of open source data science data sets and building a data science portfolio as the most crucial part of cracking this data science interview round. The interviewer is trying to understand a candidate’s knowledge of fundamental data science concepts and how he/she implements these concepts in solving real-world data science problems. This interview round is an opportunity for the candidates to show off their depth of understanding.

Say for instance you are being interviewed for a streaming platform like Netflix, a real-world problem-solving question that could be asked in the interview is –“ How would you recommend movies or TV shows to users ?”. The reason why employers ask such open-ended questions is that they are trying to understand how and what kind of data you collect to how you productionise a model. Typically these questions are asked to make sure you have the skills you claim on your CV and to check how you think and how you will be as a peer on their team if hired. Often, interviewers ask questions based on the problems they are working on themselves and if you are an experienced professional you are expected to have that acumen to solve the problem. 

Pro Tips to Ace the Real-World Problem Solving Data Science Interview Round

  • Even if you do not know the exact solution to the problem, explain how you will approach the problem, what factors you will consider, the techniques and methodologies you plan to use to arrive at the solution. Make sure the solution you frame is in a data science context and sells your problem-solving skills to the interviewer.
  • Emphasise on applied learning from end to end when you are building your data science project portfolio and touch all the concepts right from data collection, data cleaning, data pre-processing, model development, and deployment. 
  • Work on at least 2 to 5 projects in your domain so the interviewer can choose to ask questions based on that making it easy for you to answer questions on the problems you have already solved.

Data Science Interview Round #4:  The Quantitative or Math Interview Round

Not all companies have the math interview round but some companies have this round to test a candidate’s math and statistical skills which are key to data science job roles. Often interviewers ask some simple probability questions or questions based on descriptive statistics. Some of the concepts around which the questions could be framed are conditional probability, Bayes Theorem, Binomial Distribution, Normal Distribution, and Central Limit Theorem. For example, if you do not know Bayes Theorem, it is implied to the interviewer that you don’t understand the working of the Naïve Bayes algorithm. Or say, you are asked to explain the loss function of logistic regression and it could be inferred that you cannot understand how logistic regression behaves when there are data anomalies or outliers.

Having a good foundation on the understanding of important Math and Statistic concepts is enough. It is not necessary that you remember all the formulae by heart. Formulae are important because they tell how algorithms behave and it is important that you know the basic formulae. You should know how to derive complex machine learning formulae using the basics of Algebra and Geometry. The interviewer does not care if you remember Bayes Theorem by heart or not but you should know what Bayes Theorem actually is and how it works.  For example, the interviewer does not expect you to know the primal and dual of SVM but expects you to derive it in 5 to 10 minutes.

Pro Tip to Ace the Quantitative Data Science Interview Round

  • Build a strong high school level math and statistics foundation. This interview round is just a test of your strong basics and you are most likely to be asked questions on the introductory level because if you have strong basics then your explanations will be well thought-out.

And that wraps up the various interview rounds in the data science interview process. The interview rounds mentioned here are just some of the most common rounds that every company follows. One can have more or fewer interview rounds throughout the data science interview process and this depends on company to company, a project to project, and experience levels. Whatever the case may be, we hope you now have detailed insights on how to prepare for a data science interview and what you can expect to face in your next data science job interview.

After the Interview 

Once your in-person interview is over, regardless of how you performed, maintain your professionalism and send a thank-you note. Data science is a tight-knit community, so wrap up on a positive note.

5 Tips to Ace a Data Science Interview

1. Identifying the desired Data Science Job Title

The primary step to take while beginning your data science job search is to identify the various data science job titles you should be looking for. This gets slightly complicated for the reason that there are plenty of commonly used job titles that involve data science skills. However, the big three traditional data science job titles include data scientists, data engineers, and data analysts. Decide on the job role that best suits your data science skill set before you begin your data science interview preparation.

2. Hone your Data Science skills

Having identified the desired job title, it’s time to improve the data science skills required for the job role based on the data scientist job description by building real-life projects and a data science portfolio. Gleaning meaningful insights and extracting real business value requires mathematical know-how, a plethora of technical skills, critical thinking, storytelling, and intuition. One cannot be a master of all, so focus on the skills based on the data science job title you have chosen. 

3. Know about Machine Learning Algorithms

Machine learning is a crucial part of data science and it is the complex algorithms that are the core ingredient of machine learning. Understanding the math behind these algorithms and having a basic level understanding of how each algorithm works is needed to ace any data science interview. Make it a point to show that you understand the differences between the various algorithms. Also, you should know when to use one ML algorithm over another.

4. Strong Grasp of Data Science Tools

Becoming a curious Sherlock Holmes of data is not easy but if you know which data science tools to use to enhance performance, it can maximise your productivity as a data scientist. When we say data science tools, it means all tools that can help turn data into real-world actions which could include programming, machine learning, database languages, statistical tools, visualisation tools, and any other domain-specific tools. Having an inside-out knowledge of various data science tools is critical to unite machine learning, statistics, analytics, and other related concepts to make the best use of data. 

5. Create a Data Science Portfolio

Data science portfolios are becoming more and more common in the data science industry as competition for roles gets stronger. So, build a great data science portfolio before attending the interview. A great data science portfolio helps you get a steady flow of interview calls without you needing to reach out to prospective employers.  Choose real-life data science projects that can be applied in everyday life for your portfolio that showcase a well-rounded set of data science skills along with your passion and capability in data science industry. A well-documented portfolio of projects completed by you for academic, self-learning, or as a part of any data science course makes you stand-out in the interview. Other additional items that can be included in a data science portfolio are – testimonials, participation in data science hackathons, vlogs, blogs, and talks on novel data science concepts.

Recommended Resources for Interview Preparation

Interviewing is a skill of its own and the more data science interviews you attend, the better you become. You might have all the data science skills the interviewer is looking for but at times those skills might not transfer 1:1 when you are in the closed interview room with the interviewer. It’s all about experiences. Practice, Persistence, and Preparation are the key factors that determine the outcome of your data science interviews. Before we wrap this up, here are a few resources that you should go through to increase your chances of acing your next data science job interview –

Springboard provides an in-depth project-driven and 1:1 mentoring-led data science online program that comes with a job guarantee to help you land a top gig as a data scientist.  Right from learning data science programming languages like Python and R to visualising data using tools like Tableau /QlikView – the program is a one-stop solution for all data science aspirants. Springboard students also build a data science project portfolio as a part of the course which gives them the best chance possible to ace any data science interview. Check it out now!