A data science team can be an immense value-add to an organization, but, although many organizations are jumping on board, hiring data scientists can still present a significant challenge. Despite the recent influx of professionals into the data science talent pool, data scientists still remain incredibly difficult to find and hire, especially now with so many opportunities available to them.
1. Looking for a lead? Make sure they play well with others
For teams looking to hire a data science lead, look for the ability to influence senior leadership in addition to technical ability, especially if they will be in charge of developing a capability.
This individual must be technically skilled enough to mentor and manage the growing team, but they also must be able to evangelize the use of data science, nurture partnerships and trust, and work cross functionally with the senior leadership team.
2. Sponsoring visas opens up your options
As we have pointed out in previous reports, sponsoring or transferring visas provides access to the entire data science talent pool.
This is especially true amongst individual contributors with 0-3 years’ experience, where 42% are on some form of visa (see Figure 21 on page 29 of our report). Visa candidates can often be more flexible on location than U.S. citizens, who may have established family ties to a single area. Visa sponsorship can be especially impactful for companies that may not naturally attract huge numbers of applicants, in contrast to companies like Google, Facebook, or Amazon.
3. Be flexible on your list of “must-haves”
Given the “unicorn” moniker and higher salary bands, it may be tempting to create a lengthy wish list when hiring data scientists, but the availability of talent is still limited, particularly at the senior end.
We’ve seen many companies hamstrung by their inability to compromise on nearly-impossible-to-find combinations of skills and experience requirements, passing up on many strong candidates in the process. This is especially true when searching for specific domain experience or precise educational backgrounds. Consider being flexible on a couple of your requirements and you might be surprised at how it opens up the talent available to you.
4. Consider the message you’re sending to potential talent
It is important that data scientists know their work is valued, so carefully consider your reporting structure.
Having a Chief Data Scientist that reports directly to the CEO will send a clear message to potential candidates that data science “has a seat at the table”, but it is also common for data science teams to report up through marketing or strategy. However, if the team is placed under IT, prepare for what might be a difficult recruiting cycle. Data scientists are skeptical of an organization’s understanding of their work when the team falls within IT, alongside functions like business intelligence and data warehousing, which are very different. As you can see in the full report, data scientists’ salary bands vary considerably from those of IT professionals.
5. Data scientists have options, so sell your opportunity!
Data scientists who are on the market will have many career options, so sell your opportunity.
These professionals continue to be approached by corporate and agency recruiters weekly, and will likely be evaluating multiple offers. This means it is important to court your candidates, and to show them that their work is valued, understood, and put to use by your business. This should be reflected in everything from salary bands to reporting structure to the tools and technology available, which brings us to our next tip.
6. Data scientists value access to cutting edge tech
To demonstrate commitment to data initiatives and to best position data scientists for success, organizations should provide access to the latest tools and technology.
While having the latest technology is appreciated by many predictive analytics professionals, being at the cutting edge is a necessity for data scientists. The results of our 2017 SAS, R, or Python flash survey showed that data scientists overwhelming prefer open source tools, with 97% choosing R and Python over SAS, much more so than others in predictive analytics, where 62% prefer R or Python.
Given two roles that are similar, a data scientist may choose which organization to join because one offered more advanced tools and data infrastructures with which to work. Data scientists understand that falling behind on the latest technology can severely impact their longer term careers. Giving data scientists free rein to choose the tools they work with, and providing the resources they need to do so, is always a plus.
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