Employer-ready candidates understand how to speak confidently about their technical and transferable skills in ways that sell their professional value to employers. View a more detailed interview prep guide in the Behavioral Interview Prep Guide.
Tell me about yourself.
Think about your past, present, and future. Discuss your past professional experiences, and how they led you to a career in data science. Share your trajectory in the field and how you hope to grow in your next role.
Why do you want to work for this company as a data scientist?
This is a chance to demonstrate your research on the company and their data science practices. Share what you like about the company culture and how they work with data. Explain how you would be a good fit for their team.
What strengths would you bring to this role?
Choose a soft or technical skill that is relevant to the position you’re applying for. Contextualize your answer with a story that demonstrates this strength.
Tell me about a challenge that you faced at work, and how you overcame it.
Choose a challenge that involves a soft or technical skill. Explain how you overcame the challenge, how it helped you grow, how you would approach a similar challenge if it comes up again.
What makes you unique? Why should we hire you?
This is a chance to distinguish yourself from other applicants. Choose a soft or technical skill that sets you apart. Ask yourself, what do I want the interviewer to remember about me? Contextualize your answer with an anecdote that demonstrates your unique qualities.
What tools do you plan to use in your role as a data scientist?
The interviewer wants to know which programming languages and data tools you are familiar with. Be sure to discuss any tools you have experience with that are listed in the job description. Discuss a recent project in which you used one of these tools.
How do you organize large sets of data?
Most data science positions require the management of large sets of data. Demonstrate that you have experience gathering, organizing, and processing large datasets. Be sure to mention the tools and techniques that you’ve utilized.
How would you handle a situation in which you saw an error in your data or model?
The interviewer wants to know that you will not shy away from or ignore errors. Demonstrate your ability to identify and correct errors by sharing a relevant example of a time when you dealt with an error.
How do you handle outliers in a dataset?
You can discuss a few scenarios. If necessary, it’s also appropriate to ask for more clarity. For example, is the outlier a garbage value, one extreme outlier, or one of several other possibilities? Demonstrate your technical expertise by discussing how you would handle some of these situations. Include a relevant example if possible.
Tell me about a recent project that you worked on.
This is a chance to highlight your role-specific skills. Try to choose a project that is most relevant to the role that you are interviewing for. Discuss the process and tools that you used. Highlight the success of the project and the role that you played.
Pro Tip: Always keep your answers positive, demonstrate what you’ve learned, and tell a story using the STAR framework (Situation, Task, Action, and Result) for behavioral questions so you can provide details about your accomplishments.