Tutorial-Pass HR Screenings
Top Behavioural Questions in Data Science Interviews
Top Question 1: Tell us about yourself.
This is usually the opening question in a data science interview. This will be your interviewer's first impression of you, so make sure you have a well-rehearsed response.
Your answer should be a quick 60 second summary of yourself, and include your qualifications, previous roles you have had, your key strengths, and how you believe
you can contribute to the company. Try to get the interviewer to be interested in your story, so that they will follow up later on with "tell me more about this."
I grew up in London, and attended Imperial College London, where I majored in computer science. Last summer I had an internship at Facebook, where I helped deploy
a machine learning tool for fake news detection. This experience reinforced my goal to become a commercial data scientist. I have a strong background in both
theoretical and applied aspects of data science, and believe I could really help your team build its data science capabilities.
Top Question 2: What do you know about our company?
This question tests how serious and committed you are to working for the company.
Recruitment offices and interviewers are swamped with potential candidates, and don't want to spend their time on candidates who won't ultimately accept an employment offer.
Use this question to showcase the research you have done on the company and your overall interest in working with the company and the team.
Know what products and services the company provides, what type of organisation they are (small, global, not-for-profit) and approximately how many years they have been in business.
Figure out what awards they have won in their industry through lists like the Sunday Times Best Companies To Work For and the
Fortune 100 Best Companies to Work For.
Go on Glassdoor and determine the company culture, whilst identifying employee reviews that resonate with you.
If given the names of your interviewers, try to connect with them on LinkedIn and see what their paths in the company have been like.
Before applying to this role, I did a lot of research into your company.
I know that you are a fintech startup that was formed 3 years ago, and has since raised Series A funding for £8 million. Your platform uses machine-learning techniques
to identify and stop fraudulent transactions, using a combination of 154 proprietary features. I know that your company won The Top 50 Most Innovative Companies of 2019 award,
and I have read a few press releases from the Financial Times and Wired about your company. I was very impressed with what I found out
and wanted to apply right away.
Top Question 3: What are your key strengths?
This is your chance to sell your abilities and personal traits. Use the research you have done about the company to highlight the
technical skills you have that could be beneficial for the company's data science projects. Make sure to mention previous courses you took, internships, or jobs you've had where you've developed
those technical skills. Additionally, identify some personal traits that are important in the workplace, such as reliability, efficiency and enthusiasm.
I am very skilled in Python, and have a lot of knowledge in building deep learning systems using TensorFlow and Keras. In university, I took
three advanced machine learning courses covering deep learning techniques, and I built a sentiment analysis tool using deep learning approaches as a personal project.
I know this role requires a lot of deep learning for building out your
drug discovery platform, and I am confident I will be able to contribute. I am also very focused and determined at work, and will be a team member that can be relied upon to deliver results.
Top Question 4: What are your weaknesses?
This question is often asked to see how you cope under stress, as most people are uncomfortable
when asked. If answered incorrectly, it can also lead the interviewer to determine you might be unable to do your job effectively.
It's ok to keep your answer short and sweet, and we suggest you choose something harmless and unrelated to the role you are applying to, such as a weakness in delegation skills.
Also try to put a positive spin on it where possible, and make sure to emphasize that you are improving upon it, so that the weakness is framed as a thing of the past.
I just graduated from university, and of course every student needs to complete their assignments and tests on their own. I am used to completing everything I am assigned
to a high standard on my own, and thus do not have as much experience yet delegating tasks to other people. I know this is an important skill to have in industry, and am reading
management blogs and trying to implement what I learn in my personal life as much as possible.
Top Question 5: Describe a team you most enjoyed working with?
The interviewer is interested in knowing what type of collaborative environment you enjoy working in and what type of team player you are.
It's good to use words such as supportive, motivated, and hard-working here. If you've done your research on LinkedIn and have found out who else is on the team,
now is a good time to showcase that knowledge as well.
I worked in a startup in my previous role, and really enjoyed the energy and creativity in the team. There was a wonderful mentorship program, whereby experienced data scientists spent time training
graduates, and I enjoyed working with two graduates on my projects. I also appreciated how supportive we all were of one another, whilst individually being responsible for delivering
significant upgrades and improvements to the models.
Top Question 6: What types of people do you find it hard to work with?
This question is testing how well you work in a team setting. The interviewer will be looking for signs you have had disagreements or difficulties with colleagues
or managers in previous roles. Avoid the temptation to mention any past negative experiences, and instead highlight positive times and personality traits.
I've been fortunate to work in great teams in the past, where everyone was positive and hardworking. I have had the most
fun working in teams where we all support and encourage one another, and where everyone is motivated to get the job done. The one thing that I have found that brings team's morale down
is when some people try to cut corners and not contribute as much as other team members.
Top Question 7: What do you think is the right amount of time to spend in a job before moving on to your next role?
Companies deal with high rates of employee turnover amongst data scientists. This costs
companies a fortune in recruitment costs and loss of employee expertise. You will give yourself a real advantage
if you stress that you are looking for a long-term career with the company, rather than moving on after a few months or year. Typically 2-5 years is an acceptable time frame.
I think a few years at a minimum is a good amount of time to spend in one job. I'm interested in finding a role where I can stay on for the next few years, as I think
my technical skills will improve the most if I work on a number of different data science projects from the proof-of-concept stages all the way to deployment and client release stages.
Top Question 8: Why do you want to leave your current job?
Make sure that the reason you are looking for a new role is a positive one, don't criticise your current manager or your current projects. Typically, a desire to work on
different types of machine learning problems, opportunities for development either technical or managerial, or changing locations to reduce commuting time are acceptable reasons.
I've been at my current company for 4 years now, and have learned a lot working on very interesting projects.
However, my current company doesn't have any projects in NLP. I would like my next role to incorporate many NLP techniques,
as I have completed a number of MOOC courses in NLP and independent NLP projects, and am really interested in
continuing to develop and apply my knowlege and skillset in this topic.