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contractors for data science

Why you should consider contractors for data science

If you’re like many of the analytics leaders and HR professionals we talk to, you know how hard it is to build a strong data science team. Given that everyone is chasing a few fish in a small pool, it’s hard to find strong candidates with the skill-sets you need and to then win over the other companies fishing in that same pool.

 

However, many companies are focused on hiring permanent employees for their data science roles when, in some cases, contractors might provide a convenient, more readily-available solution while also providing other advantages.

 

Why contractors?

 

Project specific needs: Depending on the structure of your data science organization, contractors can help with project-specific needs.  You can ramp up the bandwidth of your team and then decrease it as your workload evolves.

 

Affordability: A common misconception is that contractors are vastly more expensive than full-time employees.  But once you consider taxes, benefits and bonuses, it turns out that the premiums associated with contractors are less than you might imagine.    

 

Larger talent pool: We are frequently inundated with resumes of people with some data science experience that returned to graduate school to formalize their skills and are now looking for their next job. Many of these people are immigrants and temporary residents. This group represents a large, relatively untapped talent pool.

 

While your company, like many these days, might eschew sponsoring immigrant visas, contracting provides you with access to this talent pool.  If you work with the right staffing agency, they can take on most of the paperwork and expense of sponsoring these people, eliminating that hurdle.

 

As we would for any position, we recommend a strong vetting process (or a vendor that implements such a process) to ensure that you’re getting what you need. But, with that process in place, considering contractors might greatly expand your options.

 

Capitalize on retention trends: Of course, most organizations are keen on owning the talent that they find and there is good reason for that – but hiring an employee outright does not guarantee that they will stay with your organization any longer than a contractor will.  Data scientists seldom spend more than 2 years with a given company anyway and you may well find that contractors are easier to keep with your company as they are not competing in the full-time employment market.

 

Try before you buy: As you’ve probably seen, interviews sometimes fail to reveal the true abilities or shortcomings of a candidate.  Contracting a person before bringing them on as a full-time employee allows for a more thorough evaluation of their technical aptitude as well as their ability to gel with the organizational culture.  

 

And while many companies are eager to hire data scientists, they don’t necessarily fully understand what those data scientists will be doing in their organization. Contracting can help you shape individual responsibilities of team members and overall data strategy without committing resources to full-time employees.  

 

So…

 

With other analytic technologies, companies build a core of talent to interface with business users, set technology directions, and oversee delivery. Once this core is emplaced, these companies then fill out their team with contractors, allowing them to flex their team sizes to meet the challenges facing them at that moment.

 

Should data science be any different? Do you need a core staff of employees on your data science team? Almost certainly yes. However, we are seeing more and more organizations pursuing employee-only approaches when they would be able to move quicker and more effectively if they gave their staffing approach some thought and nuance.

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