Interviews can be stressful for anyone, whether fresh graduates or senior professionals. Data science interviews are no different. In fact, data science interviews can be even more intimidating as the field demands highly skilled professionals who can talk the talk and walk the walk. So, how to prepare for data science interviews, to ensure that you are at your best and make an impression on the employers? That’s what we’re going to explore in this blog post. Typically, while talking about interviews, people only discuss the conversation itself. However, that’s only half the challenge. Even before preparing for an interview, you first need to make sure you get one. Here’s how you can do that.
How to Prepare for Data Science Interviews: Preparation Phase
If there is one thing that is critical for you in order to find a job, it’s your resume. Recruiters and employers go through hundreds of resumes for every single vacancy, before inviting a handful for interviews. To be shortlisted for an interview you need a strong resume.
#1 Create a strong resume
The role of a resume is to convince the employer that you are the best person for the vacancy they have. So, create a resume that:
- Has a strong professional summary, outlining all your relevant skills, experience and accomplishments.
- Speaks to the experience you have in the field.
- Highlights your skills sets in Python, SQL, machine learning, predictive analytics and other relevant technologies.
- Showcases your domain experience whether you’re an expert in banking, e-commerce, logistics and so on.
- Directs the employer to a portfolio of your work, online profiles such as GitHub, etc.
To learn how to build a good data science resume, read our earlier blog post.
#2 How to Prepare for Data Science Interviews: Apply for the right jobs
If you hunt for jobs like playing the lucky draw game, your chances will be one in a million. However, if you have a robust job search strategy, you can spend your time efficiently and be invited to more interviews. Therefore, while applying for jobs, make sure that:
- You have the necessary data science skills. You don’t have to have all the skills. But a 60-70% match is expected.
- If it is in your area of interest. Try not to apply for jobs if they don’t interest you. You will be much more conflicted if you get an interview or an offer.
- You live in the city the role is in, or are willing to relocate.
- You follow the right application method. Some jobs require you to fill a form on their portals, some others would like to receive an email. Read the instructions clearly and follow them.
Remember to customise your CV. Don’t send the same generic CV to every role. Make sure you edit it to demonstrate your suitability. Most candidates apply for jobs and wait endlessly. You don’t need to. It is an acceptable practice to follow up on your application or seek feedback.
#3 Impress in the introductory interview
After COVID-19 struck, every interview has been either telephonic or through video conference. So, it is really important to impress the employer. We will discuss the main interview later, but here are some pointers to keep in mind for the introductory conversation.
- Make sure you have good quality headphones or earphones for the call. The quality of sound on a speaker can be very bad, try never to take a call this way.
- Find a silent place to sit down and have a conversation during the scheduled time. If you are out or in a noisy place, when an unscheduled call comes, it is okay to request to speak another time. Remember, being impressive is more important than being immediately available.
- You will typically be asked about your experience and past projects. Speak confidently.
At this stage, you’ll either be shortlisted for the next round, or not. If you haven’t heard back from your employers, feel free to follow up. If you’re shortlisted, prepare for the next round.
Data Science Interview Preparation: Coding Test And Technical Interview
It is here that the interviews get intense — you will be tested for various technical and soft skills. It is extremely important for you to be prepared with the right answers.
#4 How to Prepare for Data Science Interviews: Brush up your skills
You might not remember everything you learned in your data science course. This is normal. However, it would be naive if you didn’t brush up your knowledge.
- Go through the job description and understand the skills they need. Revise everything relevant that you have learned before.
- Look at the company website to know the domain/industry they are in. Read about the domain to get some basic understanding.
- Practise Python, R, Java coding skills. Write 1-2 small programs to bring back the mojo.
- Look through your portfolio and recall the projects you’ve worked on. You might be quizzed on these in your interview and it’ll be embarrassing if you don’t remember.
Take this role at Mercedes-Benz AG for instance.
If you are preparing to interview for this role, you will be brushing up your skills in Python, R, Java, MapReduce, Hadoop, Hive, Spark, MongoDB, PostgreSQL, Cassandra, Axure, Power BI, Tableau, etc. Don’t worry if you don’t have all these skills. Make sure you’re proficient in a few and have a general knowledge about the others.
#6 Study the commonly asked questions in data science interviews
For an entry-level position in data science, there are some questions that are commonly asked in interviews. Conduct some internet research to identify these and practice answers for them. Springboard experts have given multiple online sessions and written blog posts about data science interview FAQs.
- Top 13 Data Science Interview Questions and How to Prepare for an Interview — Chirasmita Mallick, Senior Data Scientist at G2.
- 15 Data Science Interview Questions and Answers — Arihant Jain, Lead Data Scientist at ZestMoney
- Top 15 Data Science Interview Questions and the Best Way To Answer Them — Mitesh Gupta, Senior Data Scientist at Digite, Inc.
- Python Interview Questions and the Best Ways to Answer Them
Our experts have given answers to some frequently asked data science interview questions. Learn them and practise them. Understand that these are not just technical questions. There is a soft-skills element to how you solve problems, present solutions, collaborate with the team, etc. Have a list of questions for your potential employer. This will show them that you are interested in the role and want to make sure it’s a perfect fit. End on a positive note, thank them for the opportunity and request their permission to stay in touch.
#7 Practise for the coding test
Most data science job interviews will have a round where you’re expected to write code on the spot. If you’re attending an in-person interview, you might be given time and space to do this. If you’re doing this remotely, they might expect you to stay online while you’re writing this test. Remember that coding tests are not just for your data science skills, but also your critical thinking and problem solving skills. So, understand the problem and write the best solution for it.
Some commonly given tests are:
- In a transactions table, return the latest transaction for all customers. Or identify the distribution of transactions over 24 hours of the day.
- Given product price, retention and an average life time of a customer, calculate their average lifetime value.
- Given marketing data, identify the most cost-effective sales channel.
- Given public data, predict some values. This might be predicting house prices from historical data or rainfall from weather data.
- Predict fraudulent transactions based on historical data.
Learn about some real-life data science projects here.
#8 Gear up for the HR round
Typically, HR will look for an indication that you’re the right cultural fit for the company. To clear this interview, it’s important to know the company and its values.
- Visit their website and read about their policies, practices, case studies, etc.
- Think carefully if you’re indeed the right fit. If yes, then think of examples from your past that can demonstrate that.
- Practice your answers. It is important to choose the right words and practise will help.
- Clarify your expectations — in terms of salary, joining date, notice period, etc.
At this stage, your application and interview process would be complete. So, close on a positive note and thank your recruiter for the opportunity.
Data Science Career: Post-interview Follow Up and Feedback
Always send a thank you email at the end of your interview. Include specific points of gratitude to all your interviewers and express your enthusiasm for working in the company. If in a week or two, you don’t receive any word for your employer, do follow up. Send a polite email enquiring if they have an update for you. If they make you an offer, congratulations! If not, seek feedback. Learn from this experience and prepare better for the next interview.
When you approach an interview, it’s important to think of it as a process and not as a goal. Each interview will offer you a unique experience and open your eyes to how data science works in the real world. While it’s great if you can land the first job, don’t get discouraged if you don’t. The first choice is not always the best. Your subsequent interview will always be better than the previous one.
If you’d like personal coaching to make a career transition into the data science field, consider Springboard’s Data Science Career Track program. In addition to job-ready curriculum, hands-on projects, 1:1 mentorship, it also offers a job guarantee. Apply Now!