Hiring data scientists with intention
https://scientistemily.substack.com/p/inclusive-data-science-hiring
In this post, we want to contribute to the evolving discussion by highlighting three areas that are worth putting some intentional focus towards to hire Data Scientists more inclusively:
- Writing a focused job description that matches your team’s needs
- Being strategic in sourcing to go beyond your network
- Designing a structured interview process so that you can be consistent in evaluating candidates.
Writing a focused job description
To create a more needs-aligned job description, start with a longer-term vision for the data team based on how they should ideally be positioned in the company. This will vary given the organization’s data maturity; for example, is the data team in a very nascent stage, where you need full-stack data scientists who can tackle many different problems in a rapidly changing environment? Is the team underwater with ad hoc requests, where you might be able to leverage more junior Data Scientists who are eager to learn, and would grow as the team evolves to a more proactive state? Do you need to build out more foundational data models that can help enable data democratization, where Analytics Engineers would be a better fit than Data Scientists?
Counter-intuitively, it’s helpful to go through this thought-exercise without considering the current roles of the people you have on your team. Instead, this is a great opportunity to take a big step back and freely think through what the team as a whole needs to look like, and then plan steps to get there. With your goals in mind, assess your current team for strengths and weaknesses, and determine where you would like to bring in some new skills, and at what experience levels.
Being strategic in sourcing
Something we discussed at length as we were writing this was how harmful bias can creep into the practice of leveraging networks for sourcing data science candidates. As humans, we are subject to in-group bias, that is, we naturally give preferential treatment to people who are in the same group as us, and our own network can often be a reflection of that. If your team lacks diversity, only looking to your own network while sourcing potential candidates will likely perpetuate the issue.
Designing a structured interview process
Set up an objective scoring rubric
We chose to set rubrics at the level of the interview rather than for specific questions, gave the interviewers the standard questions ahead of time, and asked them to prep by seeing how they would answer those questions themselves. This way, the conversation was more casual, and interviewers could keep poking until they felt that they had assessed the candidate according to the criteria.
Signal over noise
There were some really great points made in this talk by Brooklyn Data Co. at Coalesce 2021, and the section on “maximizing signal” in interviews particularly resonated. Writing interview questions is an art, but it’s also the most important time to make sure the interview tests for what you care about most. Otherwise, fantastic candidates may not be given the chance to shine.
- Coding skills: Data Scientists rarely start from a blank CoderPad on the job, and would likely use pre-defined functions where possible. Think through what kinds of code your Data Scientists need to write on the job, and set up a paired-programming interview that would mimic that experience.
- Data intuition: Instead of asking textbook statistics questions, use some mock data visualizations to walk through what kinds of narratives a candidate would build, and how they would check for biases in the data story.
- Communication: In a business setting, Data Scientists often have to explain their findings to non-technical audiences. Instead of using solely “Tell me about a time”-type questions to assess communication, ask them to describe a technical topic that they are familiar with at a high-level.
When all is said and done, as a hiring manager, you have to make a choice, which means that sometimes you will have to say no to perfectly good candidates. Data Science is such a small community, and a person’s experience in an interview will give them a lasting impression. Make sure it’s a good one.