10 Position Clarifying Questions to Ask At A Data Science Interview

job + productivity advice job interviews Feb 02, 2023
Data Scientists at an interview

In previous blog posts we have discussed how to answer various questions that an interview or employer might ask you. In this blog post I want to address how to ask clarifying questions so that you know exactly what the data science role will require of you.

There is inevitably a point towards the end of any interview where the panel will look at you and ask do you have any questions for us. After this article I hope that you will confidently answer yes, I do.

Clarifying The Basics About The Role + Workflow

One mistake that applicants make when interviewing for open positions is making assumptions about the position. We make assumptions about the hours, about the tasks, about the tools, about just almost anything. Expectations of positions in data science, machine learning, advanced analytics and artificial intelligence differ at each company and sometimes differ within a company by department. Asking clarifying questions about the role + workflow reduce uncertainty when deciding if the role is going to be a good fit. Here are 5 examples of role and workflow clarifying questions:

  • How does this role fit into the larger organizational structure?
  • What kind of working hours/schedule is expected for this position?
  • How much autonomy will I have when completing tasks? 
  • How will success be measured in this role?
  • What does the career path for someone in this role look like?

Of course, these questions should only be asked if they were not previously discussed in the interview. Ensure that you take good notes (mental or written) so that you can avoid asking questions that you or the interviewer previously discussed.

Questions About Data Challenges

To gain insight into what kind of technology stack is used by the organization, as well as any potential pitfalls that may arise during your tenure there, ask these questions. You can also use this opportunity to show off your knowledge and skill set.

  • What type of data sets do I need to be familiar with? 
  • What tools do I need to know to work effectively? 
  • Are there any common issues that arise when dealing with large datasets? 
  • How often is the data updated? Will I be expected to manage the updates?
  •  Are there any problems or challenges that I should be aware of before starting work?

Asking these questions will mitigate many possible surprises when finding a data science, machine learning, artificial intelligence or advanced analytical position that works for you and your life.

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