It seems like every company wants to hire a data scientist, but identifying and attracting the right man or woman for the job is often more challenging than hiring managers anticipate. That’s because data science is a complex, multidisciplinary field with a small (yet highly diverse) pool of talent — and finding the right fit requires a precise mix of preparation and process.

On Thursday, September 7, in a free webinar, COO and Co-founder Jonathan Beckhardt will be sharing the best practices we’ve gathered over years of hiring data science talent. “How to Hire the Right Data Scientist” is being presented in partnership with executive recruiting firm Burtch Works, and will be cohosted by Linda Burtch, Burtch Works’ founder and managing director. 

During the webinar, Jonathan and Linda will touch on some of the biggest challenges companies face when recruiting, assessing, and hiring data scientists (as well as how companies keep the data scientists they’ve hired happy). Here’s a little preview of what will be covered:

1. Deciding if your company is really ready for a data scientist. With the average data scientist salary in the six-figure range, most companies can’t afford to make mistakes. Yes, data scientists can change your business for the better, but only if you already have the right processes in place, like data collection and business intelligence capabilities. (You can read more on this subject in CEO Ian Swanson’s article, “Are You Ready to Hire a Data Scientist?”)

2. Identifying your data science goals and matching those goals to specific data science skill sets. No two data scientists will have the same set of skills — in fact, data scientists surveyed by O’Reilly Media for its 2016 Data Science Salary Report had expertise in 17 different programming languages, as well as dozens of different relational databases, big data platforms, BI dashboards, visualization and machine learning tools, and more. It’s vital that you already have a clear picture of what you’d like a data scientist to accomplish and how, otherwise you could end up hiring someone with the wrong skill set.

3. Building an interview process that assesses both technical and non-technical skills. The popular phrase “unicorn data scientist” refers to the mythical combination of statistical knowledge and business acumen every data scientist supposedly possesses. During the webinar, you’ll learn how to create an interview process that evaluates a data scientist’s ability to do the technical aspects of his or her job, as well as his or her aptitude for communicating recommendations and results to decision makers.

4. Attracting and retaining quality talent. IBM predicts that demand for data scientists will only continue to grow, with job openings for data scientists, data developers, and data engineers reaching nearly 700,000 by 2020. But with demand already far exceeding supply, attracting and retaining data scientists is a challenge all on its own. Find out what data scientists look for in a position initially, and what company benefits make them stay.