By Katherine Olowookere

Jun 30, 2022

Preparing for a data science job interview is never an overnight event. Data Science job interviews can bring tension and anxiety to anyone with (or even without) prior experience in a data science role. Every interview is a new learning process. To make a fantastic first impression, you'll have to adequately prepare for the interview. As the popular saying by  James Baker goes, “Proper Preparation Prevents Poor Performance.”

To help you ace your interview, in this blog, I am going to share with you important tips and processes on how to successfully prepare before your interview, what to do during your interview, and the next line of action after your data science job interview.

Major Things To Do Before Your Data Science Job Interview

  1. Create an impressive resume:  While preparing for a data science job interview, the most crucial thing to remember is to have an impressive résumé. A recruiter looks at a résumé for 7.4 seconds on average. That is the amount of time before reaching a decision regarding a prospect. So, if you're interviewing for a job, you only have ten seconds to make a strong first impression. To do so, you'll need a CV that's clear, succinct, specific, and impactful.

    You can show the recruiter why you're the best candidate for the job by tailoring your CV to the position. This will provide you with a positive first impression, putting you in an excellent position to succeed in the interview. Your talents and expertise should be accurately reflected in your resume.

    Important information that should be included in your data science résumé should include your name and contact information, career objective/summary, work experience, projects, skills, education, awards, and recognitions.

    Your technical and behavioral skills must be included in your resume. Remember to always be honest about your technical skills.

    In creating a good resume that stands out, ensure you do your research about whom you are sending your resume to (i.e the recruiter or the company), choose a good template then design, customize and organize your resume layout.

    Also, remember to keep your resume in a well readable and shareable format.

    Here are sites where you can find Data Scientist resume templates.

  2. Research the company and the role you are applying into

    Having a clear understanding of the role you are applying to and the company's expectations will go a long way in helping you prepare adequately thereby increasing your chances for success during a data science interview.

    A clear understanding of the role you are applying for, the job description, and the company you are applying to enables you to predict and forecast topics that will be discussed during the interview and also to better select topics to focus on while preparing.

    If during your research you discover that the job role requires certain skills which you haven't practiced well or are deeply rooted in, you might want to review it before the interview so you can confidently explain it. Read and reread the job description and know whom you are going to be working for and working with. The best way to know more about the company is to read from

    • Their website
    • Their tech blog
    • Social networks like Twitter and LinkedIn
    • Trustable News
  3. Proper positioning: Proper positioning improves your chances for success. Understand that there are several other people applying and interviewing for the same role that you are applying for. Therefore, it is important that you set yourself apart by positioning yourself in the interview as the standout candidate. Analyze your past accomplishments, and project successes and provide examples to demonstrate how you are a team player, dependable, or trustworthy.

    Employ the art of storytelling to paint a picture of yourself that helps the person doing the interview have clarity on who you are and what you can bring to the job.

    Ask questions. Yes, you will be asked questions to help the interviewer determine if you're the best candidate for the job, but you should also be prepared to ask questions that will help you discover if you're the best match for the job.

  4. Getting in touch with recruiters and the screening call

During Your Data Science interview 

  1. Preparing for and handling technical questions asked during Data Science job interviews: It is very important you know and understand the data science job roles and job responsibilities for which you are applying. Because you'll be asked certain technical perplexing queries that you have to answer reasonably and satisfactorily. During a Data Science job interview, employers ask questions with lots of coding and algorithmic computing elements. So, as a candidate, you have to be prepared to provide answers to these fundamental questions.

Although Data Science is a wide field with a variety of unique roles, the following topics are essential concepts that you must know and you can almost guarantee that they will be covered in any data science job interview. They include:

  • Coding and programming
  • Product sense and business applications
  • Statistics and probability
  • Data modeling techniques

These are all concepts that you need to know as a data scientist, so expect them in your data science job interview. Go over the basic data science concepts and questions. Here are some very useful articles with references to common questions and concepts asked during a data science interview.

Certain platforms can help you in testing and improving your data science coding skills. These platforms include:

  1. Communication– asking questions and salary Negotiation: Another important detail you should pay great attention to during your interview is how you communicate with your interviewers. Most candidates often just go about responding to questions and queries in a rather unprofessional and unstructured manner.

While it is okay to speak your mind and think out loud during the interview, it should be noted that your primary approach to communication should be a structured and well-organized one. Communication is more than just talking. Companies love structured communication. Your answer must be brief and straight to the point. Don't go on a random babble about irrelevant details. Always maintain a clear roadmap to the conversation. If you are asked a question you don't have an answer to, just say "I don't know" rather than going into irrelevant babbling that your interviewers might eventually catch.

You should also ask questions during your interview and also be prepared to discuss your salary. Prepare small interesting questions that you will like to ask your interviewers. Ask about the scope of the job, and ask questions about the teams that you will be part of and working with. Your main objective is Finding a data science role that challenges you and pushes your boundaries and provides you the opportunity to grow and develop in your career.

  1. Demonstrating confidence and modesty during the interview:  Demonstrating confidence matters during your interview. Speak in the first person with an air of presumption, as though you already hold the job position.

Never include in your resume anything that you know you are not so confident of.  Of course, you have to position yourself well by painting a very descriptive picture of yourself and your skills and telling your story but remember that positioning yourself for an interview doesn't mean being fake. Yes, you want to impress your interviewers but that can be best achieved when you are yourself. Avoid the mistakes that most people make during their interviews by acting fake. Be honest about your skills and experiences and tell your story with confidence and authority.

What To Do After a Data Science Job Interview

  1. Send a thank you message: Many data science job candidates don't bother to send a thank you message after their data science job interview. A thank you note or email does make a positive self-impression on your employers and this simple act can make you stand out from other equally qualified candidates. Your thank you note or email should be sent out no later than a day after your data science job interview
  2. Follow up: The screening and selection process for a data science role isn't easy. It might take a while before your employers reach out to you again. Ensure you get your interviewers' business cards and contact info immediately after your interview so you can say thank you and follow up on the progress of your recruitment. Don't write the same email message to everyone rather send out personalized unique emails to each interviewer. Remember to keep your follow-up messages short and simple.

In your follow-up emails, reiterate your interest and, if appropriate, give examples of your data science and analytics projects.

Final Notes

The Data Science career path is a journey and to successfully navigate through to reach the top, you need the right guidance and proper mentoring.

Mentors are like the helmsman of your Data Science Career. An expert guides you through the entire job interview preparation process, mastering the right technical skills to create a good resume, up until when you are fully established in your field with practical advice and support and this is exactly what we offer at  O’Fallon Labs.

You can change your future and career job search narrative by getting proper mentoring from trained and experienced experts at O'Fallon Labs. We will also show you how to do a data science project from scratch with little to no coding background.

We are ready to help you reach your Data Science career goals faster and make your dreams a reality.

Kick start your journey now!

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About O'Fallon Labs

In O'Fallon Labs we help recent graduates and professionals to get started and thrive in their Data Science careers via 1:1 mentoring and more.

Katherine Olowookere

Katherine is a content manager at O'Fallon Labs. She is interested in writing about a varioty of topics including careers in technology. Katherine holds a B.Sc. in E. Physics. She is passionate about personal growth and making young people become better versions of themselves through personal self development

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