You’ve refined your resume, sent out an application, and got the call back - now you just need to nail your interview. To put your best foot forward, you’ll want to properly prepare for the interview so you can make a great first impression.
To help you do just that, we outline how to prepare for your data science interview by covering the following:
Before we dive into the best strategies and things to consider when preparing for your interview, we’ll cover some of the things that are likely to come up in your interview.
Despite the wide variety of unique roles in the field of data science, there are still essentials that are important to know (and that will likely come up in your interview). You’ll want to be able to show you have this foundational knowledge and experience.
While this is by no means exhaustive, below are some of topics that you can almost guarantee will be covered in any data science interview:
Data science is an ever-evolving field, with many specific roles that can vary widely depending on your industry, company, and discipline. A major component of preparing for the interview will be understanding the actual role you are applying for, including a detailed job description and an understanding of what your responsibilities and requirements will be.
From the small sample list of related job titles below, you can see how varied and wide-ranging the roles and responsibilities may be:
The more you know about the job you’re applying for, the better you’ll be able to prepare for the interview. Do as much research as possible so you know what type of data science job you’re applying to, so you understand - and can speak to - your fit in that role. This will also save you a lot of time applying to jobs that don’t fit your interests or experience.
So you’ve landed the interview for the data science job and you want to know how to properly prepare. While interviews can be intimidating, the best way to combat this is to be prepared going into the interview.
Below are the top tips to make sure you’re ready for your upcoming data science interview:
Read the entire job description thoroughly, and consider what the responsibility and tasks you’ll be performing are. From there, you can gauge the soft and technical skills that you’ll need for the job. To really nail the interview and prepare properly, you’ll need to have a clear idea of what the role is and what the requirements will be.
Look up what the interviewer does at the company; in most cases, the main interviewer will be an immediate — or close — supervisor for the position you are applying to. Researching them, their role, and critically thinking about how your roles will interact will be helpful during the interview (while also giving you a chance to showcase your interpersonal skills).
With a clear idea of what the role and job description are, you’ll be able to predict which topics will be covered in the interview, and better determine which topics to focus on when preparing. If you haven’t performed a relevant task since since you last left school, you may want to brush up on it before the interview so you know how to discuss it with confidence.
It’s also important to research industry, company, and technical terminology so you sound informed, can follow along, and can engage throughout the interview.
Some interviewers are looking for someone with the hard, technical skills required to start working right away. Others are looking for someone with the soft skills and critical thinking to learn quickly, knowing that they can train them on specific software tools they use as they go. If you can get an idea of what the interviewer is looking for, you can tailor your responses to cater toward either the technical skills or soft skills and critical thinking ability.
It’s also important to brush up on your previous experience, whether that be at a job, on personal projects, or challenging (but rewarding) school assignments. Being able to speak to tangible projects or experiences where you overcame challenges or produced a specific result can greatly help your prospects during an interview.
If you’re given a ‘scenario-based’ question, ask as many useful, information-gathering questions as you can to better frame your response. Many interviewees feel like they need to answer a question with the information given, when on the job, you will often need to ask clarifying questions to better meet your objectives and ensure you understand your assignment. Asking clarifying questions may be something the interviewer is expecting, and at the very least it will show them that you are critically thinking about the issue they presented.
If you have any ideas about solutions, mention them. Even if you don’t fully know how to implement the solution, or are missing certain components of the process. Once onboarded, these wrinkles would be ironed out, and showing that you can think of quality, innovative solutions to problems being presented will go a long way. It’s also important to factor in ethical considerations, even in made-up scenarios, as you’re showing the interviewer how you’d conduct yourself on the job.
While you do want to ‘sell’ yourself and make yourself sound appealing, don’t lie about or overly embellish your technical skills or software experience. If you don’t have SQL experience outside of the classroom, don’t pretend you do. Never blindly say “yes” to every skill they ask you about, especially if it’s a specific technical skill. The worst case situation is for them to ask you if you’re familiar with something like regression, and then be unable to answer a direct question about linear regression.
Be honest and upfront about the skills you have and the skills you don’t; you’d be surprised how far this honesty and confidence can go. For many employers, they are looking at your character and soft skills as much as experience with specific data science software and tools. Technical skills can be learned, but honesty, integrity, and dedication can’t. Show them these, and you’ll convince them you’re worth training.
Show an interest in the solutions they mention, and make note of them so you can research them after the interview. If they do call you for a follow up, you can impress them with what you’ve picked up since the interview, especially if you know there will be multiple rounds of interviews during the process.
As you start your data science career, it’s important to surround yourself with people that you can learn from. School, educational courses, and training only go so far; real-time experience is the best teacher, and you’ll want to make sure you are in a role where you can consistently develop and grow as a data scientist - no matter what your specific job is.
Ask about the team that you’ll be a part of, including your supervisor and the peers you will be working with. Finding a job that will challenge you, push your boundaries, and give you opportunities to grow and develop is extremely important for advancing your career.
If you find salary discussions awkward or discomforting, you’ll want to practice your responses, or at the very least have a firm idea of what your expectations are. It’s common for salary expectations to come up in an interview, and you should be ready for this to come up at any time; sometimes they will come up in the first interview, and other times it won’t come up until the final interview.
It is best to use a salary range as opposed to a single number, and you should have a salary in mind going into it. This shouldn’t just be an arbitrary amount that you expect, but a value that you can justify based on the requirements and responsibilities of the role, and the expertise and experience you bring to it. This means that your salary range will likely — and should — change depending on the role you’re interviewing for.
There are a number of services that are helpful in identifying a reasonable salary range for different jobs in various industries.
In some cases, you won’t have enough information or won’t feel comfortable listing a salary range. If you don’t want to, it’s okay to tell them you don’t feel confident listing a salary. This is especially true if you don’t have a lot of information about the requirements of the role, such as the weekly hours, vacation time, benefits, and more. The base salary doesn’t always tell the whole story, so make sure to ask questions when appropriate.
It’s a good idea to come to the interview with notes, and a pen and paper to record information throughout. Leave an area for you to jot down questions you think of that you don’t want to ask immediately. At the end of the interview, you can ask these, showing how well you listened and retained information (which is itself showcasing your soft skills), as well as showing how well you understand the role.
While researching the role, write down questions you want to ask the interviewer if they aren’t covered in the interview. These can be a great way to better understand the role, as well as show off how much you’ve researched the company and how interested you are in joining them. You can always cross out or ignore questions that have been answered by the end of the interview.
Common questions to ask your interviewer include:
You can also ask some more specific questions, even turning some of the questions you got back on them as an employer. This can help them consider the points you’ve made and allow you to speak to skills and experience you may not have had the opportunity to mention.
Some examples of these questions include:
Although you can’t predict all the questions you’ll be asked in an interview, you should still try to think about what will likely be asked of you. Going over practice questions and technical refreshers can be extremely helpful when preparing for your interview.
Below, we list some of the best resources for finding questions you are likely to be asked:
Now that you know what to expect - and have all these tools at your disposal - you should be able to nail your data science interview, and get the job.
