5 Common Non-Technical Interview Questions… and Answers

5 Common Non-Technical Interview Questions… and Answers



It can be difficult to know what to expect when going for an interview. Data Science interviews will require candidates to answer technical questions, and often take on technical exercises depending on the company and role you’re going for.


But often overlooked is the talk about soft skills, such as communication skills business savvy, creativity and impact your work has had in the past.


Here is a selection of non-technical questions you could get asked to help you prepare for your next big interview!



“Why do you want to work for this company?”


A big one is why you want to work for the company you are interviewing for. Before your interview, read up as much information on the company as you can. Take a look at their latest news and blogs. This won’t just help you get clued up about the company and what they do, but whether you will be a good fit. Social Media is often a great way for gauging a company’s culture too. This isn’t something a lot of people prepare for or even think about, it’s easy to get wrapped up thinking about the technical aspects for an interview, whereas knowledge on the company itself can really make you stand out.


“What are your strengths and weaknesses?”


Please never, ever tell an interviewer that your weakness is that you work too hard/are a perfectionist. No one believes this. No one ever has, no one ever will. You’re human (for now) and you need that to come through in your interview. This question is actually a great time for you to shine even though it’s talking about a negative. Know your strengths and be able to show an example of how they’ve had a positive impact. When it comes to addressing your weaknesses, show self-awareness and tell them what this is, but highlight what you are doing to improve. This shows your willingness to learn and develop… attributes companies will find favourable!



“How do you handle working with non-technical colleagues on a Data Science projects?”


Communication is key in data science, and you will need to be able to demonstrate your ability to explain technical data insights to your non-technical colleagues. If the data is not understood, it won’t be able to effect change, and the interviewer will need to see that you can indeed make an impact on the business.


You’ll need to be proficient in showing how you can communicate and translate the data into something that everyone will appreciate. Have you done this before? Show examples of when you’ve done this, and how it’s helped shape business decisions and projects.


“Where do you see yourself five years from now?”


This is your time to shine. Talk about your wider goals, your desired career progression, and passions. Interviewers will want to hire people who are ambitious, so it’s perfectly okay for you to be honest and say if you want to be in a more senior role than the one you’re interviewing for at this stage. Most importantly to remember here, is to try and contextualise your desires with the company’s you’re interviewing with. What are their values? What are they utilising their tech for? What is their overall cause and how can you still be adding value to this in five years’ time?



“Who do you most look up to in the data science community? What are your favourite websites/blogs/companies in data science?”


So loosely translated, if an interviewer asks you this, they want to know about the qualities you most admire in your peers and what your passions are within the field of data science. Try not to convolute your answer, opt for someone who will be relevant to the role you’re applying for. Or, a website where the content is reflective of the technologies you will be working on, or industry you will be working in. Whatever you go for here, make sure it is relevant. Other things to avoid are clichés here – the Dalai Lama may well be an inspirational icon, but he won’t help you land a job at a hot new tech startup.




There is, of course, no one size fits all when it comes to data science interviews, questions, and tasks but hopefully, this guide can go some way in helping you know what to expect broadly speaking.

5 Common Non-Technical Interview Questions… and Answers

5 Questions To Prepare You For Your Next Data Science Interview

5 Questions To Prepare You For Your Next Data Science Interview

Sat across from the interviewer for your dream job, you may start to feel the pressure. A sure-fire way to quash the interview jitters is to prepare as much as possible. Typically, you can segment the types of questions you’ll get asked in a data science interview; things such as statistics, programming and technical ability, business acumen, and culture fit assessment. Studying up on these will help you prepare as best you can. 


Here are some examples of what you could expect when interviewing for a data science role. Tailor these in accordance with what the job description asks for, read it thoroughly and get clued up on the desired points!


“What ML techniques do you work with? / Are these research level or production level techniques?”


What techniques and knowledge are required for the role? Your experience should match up with what is being asked of in the description if you’re at interview level so make sure you go in with examples of your experience with these.


Try memorizing 3 different examples of where you have used particular techniques and the effect that they have had. For example, if the role requires convolutional neural network experience, prepare 3 examples of projects where you have worked with CNN and the impact they had on the business or research you’ve contributed to.


“Tell me about an in-depth example of projects you have worked on from inception to completion. What was the project, how did you approach the problem, what was the end result etc.”?


Be prepared to explain your experience and impact in granular detail!


-Why the project existed.

-Your part vs other people’s role.

– Provide a step by step walk-through of what you did, what tools and techniques you used.

-End product and what it meant to the business.


Know your own cv inside out, don’t be caught off guard by questions on experience or a project that you cannot dive into and explain thoroughly!


“What’s your favourite algorithm?”


This is a tough one and which algorithms and tools you use will be totally dependent on the job you’re working on. The best approach to a question like this, is to have an answer ready before going in, that is fitting to the role you’re going for rather than trying to think of a ‘favourite’, think of the most relevant and be able to talk about it – show that you’re able to make a decision (this is also what they could be trying to figure out!), and communicate your reasons for your choice, all the while framing it to what they will desire in a candidate.


“What level of experience do you have with [programming language]?  What do you do daily with [programming language] and what was your hardest challenges with this?”


This is a great way for interviewers to measure you up alongside other candidates in terms of technical ability. The programming language they will more than likely ask you about will have been named as a requirement in the job description so make sure you go in with your answer on this ready to go. Have an example up your sleeve and be able to frame your use of the programming language in terms of how you could use it similarly in this role. If you’re not well versed in what they’re asking for, be honest and show your willingness to learn.


“What is the largest data set that you have processed? How did you approach this, and what was the end result?”


Again, with questions like this, interviewers will be looking for a deep dive into your successes with processing large data sets, your understanding of the approach and techniques used, and how the results have benefited the company. Can you quantify your results in terms of costs, revenue and time saved? If you can, make sure these are front and centre in describing the impact you had.



There is, of course, no one size fits all when it comes to data science interviews, questions, and tasks but hopefully, this guide can go some way in helping you know what to expect broadly speaking. 

5 Questions To Prepare You For Your Next Data Science Interview

Aspiring Data Scientists – Get Hired!

Aspiring Data Scientists – Get Hired!

Working in Data Science recruitment, we’re no strangers to the mountains you have to climb and pitfalls faced when getting into a Data Science career. Despite the mounting demand for Data Science professionals, it’s still an extremely difficult career path to break into. The most common complaints we see from candidates who have faced rejection are lack of experience, education level requirements, lack of opportunities for Freshers, overly demanding and confusing job role requirements.


First of all, let’s tackle what seems to be what seems the hardest obstacle to overcome, lack of experience. This is a complex one and not just applicable to Data Science, across professions it’s a common complaint that entry-level jobs ask for years’ worth of experience. Every company wants an experienced data scientist, but with the extremely fast emergence of the field and growing demand for professionals, there is not enough to go around! Our advice here for anyone trying to get into Data Science who is lacking experience is to try and get an internship by contacting companies directly. Sometimes, you will find these types of positions available with recruiters but you will no doubt have more luck going direct.

Another approach is to have a go at Kaggle competitions, write code and put this on GitHub for people to see. There are many ways you can gain experience in your spare time without this being in a business setting, in a way that a hiring manager will notice. If you have the time free too, think of offering free consultations to friends or businesses and build on opportunities like that. Go beyond publishing code on GitHub, and write a detailed post of your analysis and code on a blog, data site or even LinkedIn. This gives you even more exposure and exemplifies your deep understanding of what you do. There are also challenges for people with heaps of experience getting rejected due to ‘lack of experience’ and the truth is, is that lack of experience often translates to you have a lack of applicable experience to the role you’re applying for. To overcome these obstacles, make sure you’re reading job descriptions properly, researching the company and tailoring your resume to highlight how you are what they’re looking for.

Deciphering Job Descriptions

The growing demand for Data Scientists in a number of different industries, specializing in different fields means that it can be difficult for employers to define a reasonable, ‘blanket’ skill set required, which can lead to a lot of confusion for those starting out. Beyond knowing that a good Data Scientist needs to be a critical thinker, analytically minded, a great communicator and have a passion for the field, technical requirements and experience needed can vary greatly between roles and companies. Try not to be overwhelmed when looking at job descriptions. It’s important to remember that many companies will put on more skills and experience than actually needed into the job descriptions. So, even if you hold half of the skills they’re asking for, but make up for the rest in willingness to learn/passion for the role/transferable skills, then go for it – don’t be put off. If you’re not confident in doing so, try seeing the patterns in what is being asked for, highlighting the top required skills for the roles you want to apply for and take some time in getting better at these.

Reaching out

Many professionals, whilst having the qualifications needed, lack basic skills needed when it comes to communicating with hiring managers and recruiters. Commenting on LinkedIn posts asking for a review of your profile is not going to cut it, I’m afraid. Reach out directly to those that are posting the job adverts or if it’s a company, do some research and find the hiring manager or recruitment team. They’ll appreciate the direct approach, and you’ll be able to provide more information on why you should be considered for the role. It might seem like a good way to get noticed as CV’s can get lost in the mountains that recruiters receive… but this is where resume skills come in to play and knowing how to get yours noticed.

Resume skills

You’ve more than likely got some great points on your CV, experiences, and projects that are noteworthy but often, your CV will also be littered with irrelevant information to pad it out – especially if you’re just starting out. Our advice? Get rid of the filler, get to the point and highlight how you can make a difference where you’re applying to.

Make sure your skills, experience, and projects tell the hiring manager that you have the tools necessary to make an impact on their business and how when applying these techniques in the past, you’ve had x y z results. Quantify these results – how did it benefit the company in terms of revenue, ROI, time-saving or costs? Tailor your CV, don’t just send generic ones out. Exhibit your understanding of the fundamentals, that you have proficient knowledge of the foundations of data science and the rest will follow.

The layout is also important, hire a designer or put in some hours on free platforms out there that can help with this. Even on Word, you can create an interesting, eye-catching layout! You can see more on mastering your resume here. Another great way to soak up as much information about

Data Science is to follow influencers in your field on social media, especially LinkedIn – there are often really insightful posts, you can reach out to the data science community, learn new things, post questions and see current opportunities available.

Have you any other top tips for getting into Data Science? Please share in the comments!

Aspiring Data Scientists – Get Hired!

Data Scientists – Are You Prepared For Your Next Interview?

Data Scientists – Are You Prepared For Your Next Interview?

You’ve perfected your CV, got great experience under your belt, maybe a PhD and can wrangle data amongst the finest but just how prepared are you for your next interview?

Just the thought of the face-to-face interview stage is enough to strike fear into the bravest of us.  Here are a few things to keep in mind and stave off the sweaty palm syndrome. (Bonus tip – if you are prone to perspire through the palm, remember to use a tissue before shaking hands with your prospective future employer!) 

How well do you know yourself?

First things first, prepare to be an expert in YOU. It sounds quite basic, but you need to know all about your work in great detail and know exactly what is on your CV when meeting with a hiring manager. Be ready to provide detail at a headline level, but also be completely fluent in articulating all of the projects you have worked on in your career. It’s crucial to be able to explain the reasons for the project, your individual contribution and finally what the end result/business outcome was.

Think about all of the hard-won problems you have solved in your career; can you confidently say you have articulated them in your interviews to date? Most employers will look at past behaviors and results as to an initial indication of how you will benefit their business, so it’s important that you can project this in your interview.

Also, don’t forget what you have written on your CV too. It’s surprising how many times people get caught out on a question on something they’ve done at the beginning of their journey, or a coding language they haven’t used for a long time. Employers like to know that you are an expert (or as close to) about the things you’ve written you know about.

What’s in your toolbox?

 It’s not just about reeling off a list of projects you’ve worked on. Many employers/interviewers are going to want to know why you have decided to solve problems in a certain way.

One manager describes the interview process as an examination of theory and application. What he looks for is an understanding of “why?”. Why did you choose that algorithm? Why did you choose that technique? Why didn’t you choose to use this equally common method instead? Why did you reach that conclusion?

In this line of questioning, it’s important to know why you do what you do. Not that “well, my manager told me to do it” and more like “my manager and I looked at a range of techniques and we chose this one because…”

Other things to consider is having an understanding of the impact to business for choosing the method/algorithm/technique that you have chosen. Thinking in scale and knowing exactly if the model will productionise. Critical thinking skills are more important now than they have ever been.

What happens in the technical bit?  

Technical testing is becoming more and more common with organisations that want to know if you can back up what you say you can do. You may have some great answers to the how, what and why questions, but this isn’t where the line of questioning ends.

In my experience, technical testing ranges from pre-prepared online assessments (Kaggle, Codility, etc) to on the spot whiteboard and pen sessions. There is no black and white way of preparing for these tests, they will differ in every interview you go to.

What is important is to understand what problems the business is facing. What does the business do? What does their platform/product/application look like? Why might they be applying Machine Learning techniques? What does the job description ask for? These things can normally give some clues and indications as to what the technical testing may be about. If the job description is asking for a Computer Vision expert with solid Python coding skills…this may be an indication of where you’re going to be tested!

Is there anything else to remember? 

Managers will want to know that there is a human behind the prepared answers. What are your ethics? How well do you work as part of a team? Are you likely to clash with existing members of the team? How well can you communicate and present your findings to senior business stakeholders?

Managers will want to know what’s driving your passion in Data Science. Why are you doing what you do and what is the overall end game? It’s becoming more and more appealing for managers to understand that Data Science isn’t just a job for you. Are you submitting papers to be published at conferences? Do you partake in weekend hackathons? Are you part of any online Data Science communities? Sometimes that throw away answer about writing blog posts on deep neural nets can be the indicator to a manager that you genuinely have an interest in what you do.

Hopefully, there are some good takeaway points here. There isn’t a copy and paste method of preparing for interviews, as no two interviews are alike. Be prepared for all eventualities, but most importantly be prepared to be an expert in the most important subject…you.

Data Scientists – Are You Prepared For Your Next Interview?