How can you evaluate a candidate's domain knowledge during data research interviews?

when a candidate detailed a complex project they led, highlighting not just the technical aspects, but also their domain-specific challenges and how they overcame them. Starting the interview with such discussions can set a collaborative tone and encourage candidates to open up about their experiences and expertise.

Using case studies during interviews has always struck me as an effective way to assess a candidate's domain knowledge and problem-solving abilities. It offers a glimpse into how they tackle real-world challenges. One thing I've found helpful is presenting candidates with scenarios that mirror ongoing projects or past challenges the company has faced. This not only tests their domain expertise but also how they might fit into ongoing projects. For instance, once a candidate was given a business challenge we had recently solved, and their unique perspective on it provided us with valuable insights we hadn't previously considered. Starting with a familiar problem can also help ease candidates into the interview process.

Testing domain-specific knowledge is undoubtedly a crucial aspect of evaluating a candidate. Understanding industry jargon and domain terminology can significantly influence the success of a data research project.

Another example includes having a candidate submit a relatively simple data visualization and reviewing it in the interview. This a good way to see a candidate's thought process, communication, technical abilities, and ability to collaborate as you review and provide feedback.

Evaluating a candidate's data literacy skills is paramount. The ability to distill complex data into digestible insights is what sets a great data scientist apart from a good one. A practical way to start is by presenting candidates with complex data visualization and gauging how they interpret and communicate the underlying message.

Another effective method to assess a candidate's domain knowledge during interviews is to have them walk you through a project they've worked on. Ask them to detail the tools, data sources, and libraries they employed and their reasons for doing so. This approach helps determine whether the candidate truly grasps the underlying concepts they've engaged with.

This should apply to all job candidates, including tech leads, managers, executives. Also EQ should be considered for an efficient team working environment.

Ask person around the projects he/she/they have done. Ask them about their opinions about some hypothetical situation. Check the person on their data literacy skills.

I think one can ask for the person to walk you through the whole project from the start including the problem statement, moving to talk about the research methodologies and the approach.