Data Science

9 Reasons why people fail in Data Science interviews

March 13, 2020

Data-scientists are highly paid professionals known for their technical and analytical skills. Numerous organizations are busy building up the workforces comprising of top-notch data scientists. But, cracking a data-science job interview is no easy task. If you wish to successfully tackle a data-science interview, knowing the do’s and don’ts will help boost your chances for success.
We have listed nine crucial reasons for you to know why people fail in data-science interviews, and what can you do about it. Take a look.

See Also: How to learn data science with no coding background?

Reason #1: Not understanding the job description or job role

The number one reason why people fail in data science interviews is they apply for the position ‘related’ to data science but have no clue about the job. It is an obvious expectation that you understand the job role and the job description. While many candidates tend to take it casually, this is the first turn that can go wrong. Not being clear about the ‘role’ the position has to offer and the required skill set to undertake it is a ‘red flag’ for tech-companies. Before you appear for a data science interview, carefully study what the role is about and what is the required skillset for it. Keep the following points in mind;

  • Understand the job role: Companies might be looking for a data scientist, developer, data analyst, database administrator or a business analyst. All these roles are related to data science, but are essentially different. Research all these data science job roles over the internet. Understand the key differences. Next, ensure that your skill set matches with the listed open position. Often, IT & software companies look for developers with a scientific aptitude and business acumen. In short, an all-rounder professional persona. So, whatever the job role is, make sure you convince the interviewer that you possess these valuable qualities.
  • Be clear with the ‘job requirements’: The job description will always tell you the knowledge and skills required for the data science position. A developer needs to be a seasoned coder, an analyst must have sound command over the statistics, and a database admin must be thorough with various database management systems. A ‘data scientist’ is required to possess almost all these skills. So you should be specific with your knowledge and have a clear idea about the position you have applied for. If the position says ‘data scientist,’ you should convince the hiring manager that you have the right knowledge, aptitude, and attitude to offer them.
  • Why taking the above pointer casually can be a ‘red flag’? Hiring managers, responsible for ‘best-fit’ recruitment, often look for detail-oriented talent. Data science is such a field that requires in-depth knowledge, technical skills and immaculate attention to the details. If a candidate is unclear about the job role and its requirements, it might be a sign of caution for them. It shows that the candidate lacks seriousness, focus and he/she is not detail-oriented.

Reason #2: Not researching the company and its business

If you go out to buy a new smartphone, it makes sense that you compare and dig up the best possible options within your budget. You analyze and compare the make, model, tech-specs, and feature critically. In short, you are very thorough with your smartphone research! Then why not do the same for the company you applied for?

Often, candidates ‘go-in’ without ‘knowing’ the nature of the company, and its business undertakings. It might make them look unserious. While this may not put a serious negative tag on your professional attitude, it is better to come off as a keen and learning candidate. Keep in mind that the interviewer will assume that you know these basic details. For any data science interview, keep this checklist handy for an ‘XYZ’ company;

  • What does this ‘XYZ’ company do? How are they exploiting the big data?
  • What is the nature of the business ‘XYZ’ is involved in? Is it a service based company or a product based company?
  • What are the services or products offered by the company? How is the big-data involved with these offerings? 
  • When and who established the company? Know briefly about the founders and the company’s vision. All this information can be extracted from the ‘XYZ’s’ official website. 
  • Where does ‘XYZ’ stand in the industry? Is it an authority enterprise or a startup? Evaluate your options according to your growth prospects.

Lastly, make sure you read employee reviews from that organization. They can be easily found online. If you can, get in touch with an existing or an ex-employee of that company and know their views. Don’t stick with an entirely negative or a positive review. It is just to help you draw the picture of the work culture in that organization.    

Going through this checklist will help you boost the interview conversation. You can gather a lot of ‘intel’ about an organization. During your face-to-face round, you can even propose some insights or ask questions based on this information! You will surely come-off as a curious learning professional.

Reason #3: Not being sound on the ‘technical front.’

Call your technical knowledge and skills as the basis for a data science interview. While every other parameter you are judged on decides how ‘well you will fit into the organization,’ your command over the ‘technical skills’ for the position will decide ‘what value you will add to the organization.’ As an aspiring data science professional, your command over the subject, hands-on experience with various tools, machine languages and technologies is a must. Afterall, your aim in the company will be to analyze massive data sets and extract ‘value’ from them. Keep these pointers in mind for a data science interview;

  • Be thorough with the coding languages: And we mean not just one! As a data science professional, you should know the ins and outs of Java, C, Scala, Python, R, etc. Even if you have not worked with all of them, you should know the pros and cons of using these languages in the data science context. For example, some languages are suited for medium level data crunching, some for visualization and some are general all-rounders. Also, make sure you know how to code in at least two programming languages.
  • Go through a complete glossary of data science terms and keep the jargons and definitions spooled up. You can also refer to this updated glossary of Big data terms at any time.
  • Know your ‘algorithm game’: It is highly likely that you will be asked to write a code for an algorithm. It is recommended that you understand the standard algorithms like k-means, Apriori, AdaBoost, linear regression, logistics regression, etc. Polish your coding skills regularly. Writing a sloppy code for an algorithm here would be the last thing you want to do! 
  • Stay abreast with the latest industry trends: Internet of things (IoT), machine learning and artificial intelligence, data analytics, and open source software are some of the newest data science industry trends to be named. As a professional in this field, staying up to date with these emerging technological trends comes highly recommended. The interviewer would be glad to know your knowledge and technical-opinion about this industry and its future.

Displaying your true technical prowess during the interview will help you succeed in a data science interview. Combine it with a right aptitude, as we will see further, and you are good to go!

Reason #4: Not backing your points with stats and facts

This is a ‘data science’ job, remember? You might want to backup your views and arguments with facts. Let your ‘inner’ data scientist speak up and provide for statistical evidence. “92% of customer interaction happens over the phone”, “72% of leaders consider AI to be a business advantage” or “As of 2016, 58% of enterprise businesses are employing predictive analytics within their organizations”. Statements like these not only build your credibility, they also succeed in impressing the hiring authority. There is a simple hack to remember this aspect. Just keep this simple and sweet points in your mind;

  • Always be specific with your answers. To give a specific answer, listen to the interviewer attentively.
  • Try to break the ice with a statistical fact. Support your statements with real-world examples and use-cases. Of course, it is not always possible, but make the best use of stats and examples whenever possible. 
  • Vague and general answers are not appreciated too much. Remember to keep them to a minimum. 
  • Keep in touch with the statements of IT and software industry leaders. They often come up with factual and statistical statements. Quoting them at the right time will leave a positive impression on the interviewer.

Reason #5: Not revealing the ‘package’ you are

Hey! You are here to tell the company what you have to offer to them. It is a crucial point that many data science candidates miss out. A hiring manager would like to know all your qualities and achievements to make a fair judgment about you. An open conversation will help him/her understand your true self and the underlying potential. Here is how you can reveal yourself as a worthy data science professional to a company;

  • List all the data-science or machine learning projects you’ve worked on. From your graduation or post-graduation time till your last job, leave none of your efforts unlisted. Be it a ‘passenger survival predictor on-board the Titanic’ or ‘speech recognition engine’ you designed yourself. Your past projects, amateur or professional, will display your interest, hands-on experience and versatility in this growing tech-field. You can even ‘give yourself’ some ‘homework’ and work on the pet projects related to data science and machine learning. Do that and thank this post later!
  • Emphasize on your team efforts and achievements rather than the individual accomplishments. One man did not build pyramids, but thousands over the years. Big-data and predictive analytics are no less than the ‘pyramids’ of the digital age. Companies actively seek professionals who can work integrally with teams and deliver results. For this, you should focus on what you have achieved as a team till now! In an organization, you might not only work individually but as a manager or a mentor as well. 
  • Don’t forget to reveal your qualities and priorities as a person. The interviewer might pose questions directly to you. Like, what are your strengths and weaknesses? Why this particular job? How would you tackle an ‘abc’ challenge? Take these direct questions as the opportunity to prove your abilities and strengths. Back them up with your real-life examples. For example, you should justify your ‘analytical thinking’ by a coding problem you solved. And, your idea of professional growth should match with what the company has to offer you! Your answers should indicate your true-qualities, and long-term goals should be in sync with theirs. 

Technical interviews are meant to be driven by the candidates. This is your chance to ‘contrast’ yourself with another potential talent. Hiring managers often favor candidates that can give them ‘original’ answers and show the willingness to learn. Reveal your achievements and qualities as the conversation progresses. Recruiters will find joy in conversing with you as well!

Reason #6: Failure to empathize with the interviewer

Walking a mile in the shoes of a data science interviewer will make many points clear to you. Meaning, if you try to look from the recruiter’s perspective, you can evaluate yourself better as a candidate. An interviewer’s perspective will help you discover where you do well and where you lack! The following points must be taken care of within the discourse with a technical interviewer;

  • Giving ‘rote’ answers; This is one ‘annoying’ habit candidates throw at the interviewers. Giving same old ‘bookish’ answers to the hiring authority can make you sound monotonous, even inferior. In fact, ‘telling them what they want to hear’ is a trick they can detect quickly. So, stay clear and stay cool. You are there to talk about yourself, and honesty is the best policy here. If you stay true and present yourself genuinely, the interviewer will be able to map your thought process accurately. Try not to bore the interviewer with ‘almost similar’ answers others probably have already delivered!
  • Say ‘no’ when you should; Nothing speaks more loudly of sincerity than a job candidate saying no. if you don’t know a term, say no, if you are clueless about a question, the “I am not sure, will be able to answer it the next time we meet” approach will help. Interviewers often spot candidates who are ‘yes’ parrots and get irritated by them. Don’t be one and you will be earning points for the data science interview. 
  • Drive the interview conversation; As a data scientist, you will be working on finding ‘insights’ from tons of data. If you can do that, you can engage in a meaningful conversation with your interviewer. It should not be a ‘one-way’ drive, rather a natural flow of words. ‘Both of you’ have invested time in meeting each other. A sparkling discourse is the best value you can get out of it. You will also come out as a bold, pro-active and thoughtful professional.

Follow the above points to create a ‘standout’ impression. Keep in mind that any trick or hack you plan to ‘use’ on the interviewer has already been tried and failed. Instead, try to evaluate yourself and the others like a recruiter does. Be willing to walk the long-mile and create value for the interviewer as well!

Reason #7: Not asking the essential questions

Some people have no doubts and no questions. There are the ones who have many doubts and many questions. As a data science professional, you should be the one with reasonable and vital questions. Ask them, and you will impress the interviewer. If a candidate does not ask some critical questions, the thought process can be in question. A genuine candidate should always ask legitimate questions. Here is a set of some critical questions you can refer to;

  • Why is the position open? Why did the previous employee leave?
  • Can you brief me about the day-to-day responsibilities of this job role?
  • What are your performance expectations for this role in first three months (Or any time interval)?
  • What is the work culture here like?
  • What are the ‘hottest’ prospects company is looking at right now?
  • What is the company’s objective for the next five years?

Asking such sort of questions will reflect your seriousness and detail-orientation towards the job role. Remember to ask these important questions at the end of the interview. You should not be limited to only the above-listed questions. Feel free to express your doubts when the opportunity shows. 

Reason #8: Being a problem pointer and not a problem solver

This is one attitude the data science industry keenly observes in its professionals. Data science professionals ought to be problem solvers by default. Hiring managers see many examples of candidates who can point out problems. But when it comes to solutions, the talent does not seem bright anymore! A data science interview is your opportunity to showcase your ‘problem solving’ skills and offer the company a theoretical solution. The following will be one of the most important checklists for a data science interview. Here it is;

  • Tell the interviewer how your knowledge and skills can be utilized by the company to achieve a feat. It can be your previous job experience or even something you learned during an internship. For example, you learned ‘data munging’ and can make the company’s raw data ‘usable.’ Or, you might be well versed in multivariable calculus and help improve a machine’s prediction abilities. The point is, offer them your answers in a ‘solution format.’
  • If you have decided to ‘talk about the competitors’, then make sure you also back up your statements with a competitive strategy. This can be an opportunity for you to showcase your business acumen. For example, you might compare products/services of both the companies. But then, list how one can be improved over the other, or what is the scope of evolution for a given product. Taking the conversation in such a direction will also reflect your innovative side.
  • Offer your solutions in the ‘business language.’ After all, corporates exist for business, but with differing visions and styles. Try to frame your answers from a business perspective. For example, maybe the company can optimize its leads by using an AI in online analytics. The target customers can be studied with their purchase preferences. Hence, during a product launch, similar customer profiles can be laser targeted. Remember that you will face business-analytics problems on a regular basis.

A problem-solving attitude is the most sought-after quality in data science job roles. Don’t just point at the problems, offer a solution as well! If you are aware of the complexities and ‘bottlenecks’ of a specific technical challenge, point it out. Sit with this mindset during the interview and “thou shalt triumph over all.”

Reason #9: Making ‘rookie’ technical mistakes

A data science recruiter won’t mind a genuine mistake. But committing them frequently is a ‘crime.’ It is a straightaway red tag on a candidate’s technical competence. And this is not just limited to the wrong usage of terms. It includes ‘myths’ and misbeliefs as well. Following are some rookie mistakes to avoid;

  • Providing the wrong definition of terms and jargons. Or, using them in the wrong context! For example, calling a collection of 50,000 observations ‘big-data.’ 
  • Unable to expand more on technical topics like algorithms, databases, coding languages and business analytics applications, etc. This may indicate a ‘shallow’ knowledge-base.
  • Not knowing the ins and outs of various tools, technologies or platforms. For example, you should know which algorithm suits which case and why it is better than the others. Or, is Scala better than Python? Can we substitute R with these? You should have a sound technical opinion on such comparisons.

Avoid these mistakes and be sure about the knowledge you possess. Verify, clarify anything at anytime to make it through the data-science interview.

There! We just evaluated nine critical reasons why people fail in Data Science interviews. The listed reasons cover significant points pretty well. Work on the solutions provided for each of them. Maintain the right professional attitude. Keep your technical skills polished. And you shall land your dream job as a prestigious data science professional.