Top questions you should ask a senior data scientist

As competition increases for hard-to-fill senior technology roles, you need to be even more strategic in your hiring process. Data Science has been at the top of Glassdoor’s list of Best Jobs In America for several years, most recently being selected as the second best job in the country with a median base salary of $113,000 and 4.1 out of 5 job satisfaction rating. As unemployment plummets for senior tech roles, this demand is only increasing. 

You don’t have time to sift through thousands of candidate profiles as you work on building your modern tech team, so your hiring process should be focused on a fewer number of candidates who have the niche skills to move your company forward. The way to do that is with strategic questions that will help you efficiently assess candidates’ skills and allow you to focus on the candidates who can do the job. 

What coding languages are you most proficient in?

The best place to start is with an understanding of the candidates’ experience, comfort level with various coding languages, and willingness to learn. Filtered’s skills-based hiring software supports more than 30 coding languages for evaluations, with tests that are comprehensive enough to cover multiple skills levels within each engineering discipline.

When asking candidates about their coding language proficiencies, listen to answers that include references to data visualization tools and coding languages – and keep an ear out for responses that indicate the candidate can learn quickly.

Tell me about a data project that proved challenging and how you solved it. 

It’s important for hiring managers to get a good sense of how a candidate might handle data-related problems or errors. Filtered’s video interview process allows you to assess a candidate’s nimbleness and ability to work under pressure, while also getting a sense for how they might fit into your company. 

When asking this question, listen for answers that include what they have learned from this particular challenge and how they might do things differently in the future. 

Tell me about a time when you had to organize a large data set. How did you handle that?

A highly-qualified data scientist should be able to explain to a hiring manager how they “clean” or organize “dirty” or disorganized data in a way that allows them to analyze and generate quality insights. To take this question a step further, Filtered offers specific data science challenges that can evaluate a candidate based on a one-of-a-kind scoring rubric in JupyterHub. This data challenge can help evaluate a candidate based on data exploration, checking rows, data visualization, identifying relevant metrics, and more. The best part: these challenges auto-grade large data sets based on any rubric.

Candidates’ answers should include the reasoning behind how and why they used a particular tactic or tool to organize and analyze the data. 

Why should we hire you over other qualified candidates? 

Throughout the interview process, you should get a good understanding of the candidate and their technical skills. It’s also important to understand how a candidate will fit into your company’s culture through their values, mindsets and behaviors. 

Filtered’s live video and technical interviewing interface enables candidates to demonstrate their ability to collaborate on projects and build out new projects or complete projects on top of existing code. Candidates’ responses to this question and work in the collaboration space should demonstrate confidence in their skills and commitment to working with a team. 

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Remember, hiring the right candidate is all about making sure it’s a great fit for both of you. Utilizing the latest skills-based technology, Filtered can help you automate your hiring workflow and reduce interviewing time. Start interviewing for free today and let us help you make the shift to skills-based hiring.