How to Ace Data Science Interview by Working on Portfolio Projects

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Recruiters nowadays are checking your online presence before contacting you about an interview. They will look for your LinkedIn profile, GitHub, and Kaggle to figure out what value you will bring to their company. The hiring manager will also look for the latest blogs or projects you have worked on in the past to prepare interview questions so that they can test your intelligence (catherinescareercorner). Other than that, working on real-world projects will give you the required experience for the job, and with a few projects in your portfolio, you will make a good impression on the recruiter (data-flair).

We will be learning new ways to crack your interviews and how creating a strong portfolio has helped me aced multiple interviews. I will also give you tips about improving your current projects and how recruiters look at your profile. The article is based on my personal experience and the things I learned from career coaches.

Interview #1

A recruiter contacted me after going through my Kaggle profile and told me how he was impressed by my recent competition performance. He asked me that he would love to talk more on a call. To be honest, I was surprised by how a financial organization this big contacted me through Kaggle and how contributing to competition landed me an interview with a principal data scientist.

Image by Author | Abid Ali Awan | Expert | Kaggle.

Insight: Participating in the Kaggle competition can land you a job.

During the interview, we have talked about my previous projects and my previous competitions. I explained to him that how I am actively participating in other competitive platforms such as Zindi. He asked me about the project I was currently working on and what are my future goals.

Insight: Creating portfolio projects will defend your skills on your resume.

At the end of the interview, he was quite impressed with my current performance and told me that I was the best possible candidate for this job.

Interview #2

This interview was quite different as the data scientist who took my interview focused more on my GitHub profile rather than my resume. After this interview, I realized how much recruiters are interested in a portfolio rather than a LinkedIn profile or resume.

After applying for the job on LinkedIn, I received an email regarding the interview, and during the interview, he talked about my GitHub projects. They even asked me what the problems I faced during the project were and how I solved those problems.

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Insight: Even professional data scientists maintain GitHub profiles.

After the interview, I went back to see my profile and noticed that the interviewer was focusing on the latest projects in my repository with descriptions. He also talked about my pinned projects, and I was able to answer every question because I knew every part from the start till the end of these projects.

Insight: Always pinned/highlight the projects you have worked on 100%.

Overall, the interview went smoother than I thought, and I received exceptional feedback from the recruiters as they were comparing me with Harvard Graduates.

Interview #3

The third interview was quite surprising as I was interviewed by five people from different departments, and they all talked about my Deepnote profile directly or indirectly. This interview was the reason I began to draft this article so that other people could take advantage of my experience.

After applying for a job, I received a call from the top telecommunication company in my country. My first interview was taken by three experts in their field. They asked me about my past work and my goals. After the initial introduction, we dove into detailed discussions about my projects and Deepnote profile. The expert data scientist asked me key questions about NLP and how I was using them in my projects. They went through top projects and asked me about how I solved certain problems.

Insight: Publish your best work in the form of notebooks with detailed descriptions.

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My second interview was conducted by upper management who were interested in my mindset and how I can deal with certain data. They asked me about feature engineering, and finally, one of the guys asked me about my Deepnote profile bio. He was interested in my vision of building applications that will help students struggling with mental illness. In the end, he asked me how working in the company can help you reach your goal.

Insight: The biography should be personal, and it should describe your life goals.

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Both of my interviews were excellent in terms of feedback, and the company's management was impressed with my answers about my future goals. I explained how working with them will help me grow and achieve my goals in the long run.

Tips

1. Tidy up your profile: If you have an interview related to NLP, make irrelevant repos and notebooks private so that the interviewer can focus on the projects related to the field.
2. Project descriptions: make sure the first line they read is about the project summary. Writing a description about the GitHub repo or Deepnote notebook will help the recruiter comes up with related questions.
3. Revise your project: You should go through the project description on GitHub or Deepnote so that during the interview, you can answer in detail about what this project was about and how you can overcome some of the critical issues.
4. Keep your portfolio current: This will make an impression on the recruiter that you are actively contributing to the project. This will also help you improve your skills because with a unique problem comes unique learning experiences.
5. Focus on profile bio: Write about your job experience and your current project. Writing about your goals will help you get noticed. Make sure that it’s your personal story and you care about your vision.
6. Participate in Kaggle competitions: This will improve your chance to get noticed and this will make you attractive candidate for a job.
7. Revise data science cheat sheet: Even though you know everything about your project, you might get stuck in technical knowledge such as topic modeling and how TF-IDF

Bio: Abid Ali Awan (@1abidaliawan) is certified data scientist professional who loves building machine learning models and research on the latest AI technologies, and is currently testing AI Products at PEC-PITC, which later get approved for human trials, such as a Breast Cancer Classifier.

Related:

• Most Common Data Science Interview Questions and Answers
• How Can You Distinguish Yourself from Hundreds of Other Data Science Candidates?
• How to Build Strong Data Science Portfolio as a Beginner