This blog is contributed by Brian Shepherd, one of Burtch Works’ experts in data science and analytics recruiting.
If your team is looking to hire data scientists and analytics professionals – you’re probably aware that you’re not the only one! With 82% of quantitative teams planning to hire in the first half of 2019 and 70% actively increasing the size of their team, competition for this talent is fierce.
As recruiters, we often have the benefit of an outside perspective when a company’s hiring process doesn’t go quite as planned. There are three common scenarios that frequently come up specifically when sourcing tough-to-find analytical talent, so I wanted to share some of our strategies how you can manage them.
3 Strategies to Tackle Data Science Hiring Difficulties
1. Prioritize your data science hiring needs
Since there is no one-size-fits-all definition of what makes a “perfect” data science hire, the makeup of a strong candidate often depends on the needs of the team and position.
Beware of hunting for unicorns!
While the first data science hire will often need to wear many hats, it can be extremely difficult to find that “unicorn” candidate who is an expert at everything (technical skills, data evangelism, leadership & strategy, someone who can be a player/coach, etc.), so it’s important to prioritize which skills are most important for your team, and compromise on others if necessary.
Instead of focusing on checking every box at every position, think about which skills are most important for the specific data science position you’re hiring for, and which others can be taught or learned on the job.
Keep in mind the limits to industry experience
Another key point to keep in mind is that while technical skills and data science concepts may be non-negotiable, requirements such as industry experience or business acumen can often be trained up once the new hire has joined the data science team.
Especially in the case of industry experience, it’s important to be aware of market availability when making this a hard requirement. Hiring authorities may find it exceptionally difficult to find data scientists with, say, 10 years of experience in healthcare or pharma, since many firms in these industries are still developing their data science capabilities.
Below you can see data from our 2018 data science salary report, where you can see that although data scientists are beginning to spread to other industries besides Tech, if a Financial Services firm were to only consider data scientists currently working in that industry, they could be limiting themselves to a very small portion of the available talent pool.
2. Emphasize the mission and goals of your data team
An important aspect to consider particularly when hiring data scientists, is that they’re often what we call “mission-driven”. By virtue of the fact that data science is often about searching for patterns or predicting behavior from large datasets, strong data scientists are problem solvers, analytical thinkers, and often want to know how their work is being used.
How do you appeal to mission-driven data scientists?
Although not every job will be the most exciting and cutting-edge that the industry has to offer, there are still ways to make your role appealing. Here are some questions to think about:
- What story is your organization telling during the hiring process?
- How will their work help the company or the industry/society at large?
- What are your organization’s objectives?
- Are you emphasizing innovative practices or exploring new technologies?
- How will their work be valued?
- How can data scientists on your team assist with taking their work to the next level?
3. Be cognizant of timing issues
With so many data science teams looking to hire, it’s no secret that data scientists today have a lot of options. Data scientists are often receiving multiple offers during their job search, so timely correspondence is crucial.
Don’t want to miss out? Speed up your hiring process!
Often the most common hiccup that we see companies run into is simply taking too long to process candidates. Whether it’s waiting a week after the phone screen to schedule an on-site or delaying the offer, this can seriously impact your organization’s ability to land strong candidates. Even with a very compelling offer, your team can still miss out on the talent you want due to timing issues, since the data scientist you’re considering has likely been noticed by other companies as well.
Our best advice is to respond or follow-up to schedule next steps within 24 hours, and most successful hires we see ideally take one month or less from application to extending the offer. And yes, that may include multiple phone screens, an on-site, and a technical assessment, which are also becoming increasingly common.
The quicker you can move candidates through your hiring process, the more likely it is that you’ll be able to hire the data scientists you want.
I hope this information was helpful! If you’re looking to hire data scientists or analytics professionals, or wanting to explore opportunities be sure to connect with me (Brian Shepherd) on LinkedIn.