5 Tips for Acing Your First Data Science Interview
If you are looking for a full-time, part-time, or even summer internship opportunity in the field of data science, then check out 5 simple tips on clearing behavioral and technical interview stage.
If you are a fresh graduate looking to start a career, an expert switching jobs for better opportunities, or even a student applying for a summer internship, then you must know that a job interview is a tricky part of the recruitment process. To clear multiple rounds of interviews, an applicant must showcase expertise in domain knowledge, technical experience, interpersonal skills, and understanding of the job and company. You need to know the value you bring to the company and decipher hiring manger expectations.
When I started applying for jobs, I had no idea how data science interview works. I was switching from the management field, so I thought maybe they will ask me simple questions about data science and how to manage a project. I was wrong about most of the things. The data science interviews are quite different. You need to have the knowledge of technical terms, modern frameworks, coding, business use case, and statistics.
After learning from my failure, I have curated five tips that worked for me, and I am sure they will work for you. Apart from these tips, you just need self-confidence and practice to ace any interview.
Review the Job and Company Profile
Forgetting about the specifics of the job description and vision of a company is the most common mistake that an applicant makes. The hiring manager will identify these mistakes in the first few minutes, and by that time he/she had made the mind to reject you. Why? Because they think that you are not serious about the job.
If you have applied through LinkedIn or any recruitment platform, always go back and put the time to research the company and job description. Read the job requirement, day-to-day tasks, and what companies are expecting from you. Make sure you understand the organization’s motto and work environment. Visit the official website, and look for the recent projects. This information will help you impress the interviewer, and to be honest, that is your initial goal.
The most common question asked by the recruiter is “Why are you the right person for this job?” or “What do you know about our company?”. If you haven’t prepared for the interview, you will get stuck in these questions, and it will look bad on your job application.
Review Your Resume and Projects
Nowadays, recruiters focus on portfolio projects, and they will look for GitHub or Kaggle profiles before scheduling a job interview. It helps them assess your technical skills and prepare for the questions related to your recent projects. The questions are not limited to the projects. The interviewer might ask you questions about your previous jobs and the skill mentioned in your resume.
During a technical interview, I was asked to describe everything about my recent project on Natural Language Processing. They asked me about the tools, technical concepts, and web frameworks. In the second round of interviews, I was asked about the biography on the Deepnote profile and resume. So, it is better to go back and review both resume and portfolio projects. Make sure you can answer all of the questions, even the project resulted in failure.
Revises Data Science Terms
In one instance, I completely forget about how “stochastic gradient descent” works, and in another instance, I forgot about the term “bag of words”. These situations were quite embarrassing as I have been working on the data science projects for couple of years and still couldn’t answer basic questions. To avoid this happening ever again, I made the habit of revising the technical terms using cheat sheets. Preparing in advance also made me confident about answering programming-related questions. Check out the complete collection of data science cheat sheets Part 1 and Part 2.
The cheat sheets provide byte size information about technical terms and if you want to learn more, then check out DataCamp's resource on data science with Python, R, and SQL. The resources include courses, blogs, tutorials, cheat sheets, assessment tests, and a guide on certifications.
Read Most Asked Interview Questions
There are three sessions of interview. The first is conducted by HR manager and it is non-technical focused on your strength and weakness. The second part is more technical where team manager and chief data scientist assess your technical knowledge. The last part is conducted by company’s executives and it is focused on company culture, work life balance, and growth potential.
I will highly suggest you to go read frequency asked questions in data science interview. These question are both technical and non-technical. You will be surprised to see how you were unprepared after reading 87 Commonly Asked Data Science Interview Questions.
I will highly recommend you to read Ace the Data Science Interview by Nick Singh and Kevin Huo. The book explains how the job application process works and how you can ace your behavioral interview. Furthermore, it dives deep into the technical side of data science. Focusing on probability, statistics, SQL, machine learning, coding, product development, and case studies.
Take Few Mock Interviews
Mock interviews are more like practice interviews that will improve your body language, talking points, and anxiety. I had performance anxiety where I was unable to respond to the question even though I knew the answer. I even got stuck on simple probability questions.
To improve my body language and overcome anxiety issues, I started taking mock interviews on Skilled. The platform connects you with the top industry HR manager, software engineers, and data scientists. In my case, I was matched with the hiring manager and chief data scientist. The interview has also made me aware of new opportunities in the field and how to approach recruiters.
If you don’t want to pay a fee for a mock interview, you can ask your friend or family member to conduct an interview. The primary purpose of a mock interview is to get honest feedback, and use it to prepare for your actual in-person/online interview.
