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Does hiring a freelance data scientist make sense for you?
Enterprises need to make the right hiring choices when it comes to supporting their data projects. Here are some reasons your company may or may not consider hiring a freelancer.
Data scientists are now part of the gig culture movement, but should you hire a freelancer instead of a full-time data scientist? If you lack data science talent or your existing data science team needs expertise it lacks, such as computer vision or natural language processing, perhaps you should consider a contract hire. But a freelance data scientist isn't always the answer to an organization's needs.
Overall, companies are more inclined to hire a full-time data scientist than an individual contractor. But, according to the 2021 Robert Half Technology Salary Guide, many organizations have adopted a flexible staffing strategy with a mix of full-time and contracted workers for a variety of reasons, especially the rapid economic changes we saw during the COVID-19 pandemic. The report also found 37% of surveyed organizations contracted workers to access specialized skills, while 32% contracted to alleviate burdens on their full-time employees.
Of course, there are other options. Employers could hire a part-time data scientist or use a consulting firm. If enterprises have a particularly compelling problem, such as curing a form of cancer, another option is to host a data science competition using a platform such as Kaggle. Some organizations even consult with academic resources for cost-effective analysis.
However, before hiring data science talent, it's best to understand what data scientists do because there are some nuances specific to this type of freelancer that hiring managers would be wise to understand.
What is a data scientist?
A data scientist is a data expert who often holds an advanced degree in mathematics or statistics and probably knows how to code in R or Python. The most sought-after data scientists also have relevant business domain expertise.
While skill sets vary among individuals, a data scientist's job is to help the employer solve difficult problems involving discovery, optimization and/or prediction. The role may be considered part of IT, or it may be specific to a departmental function. Of all possible data-related roles, data scientists tend to be the most sophisticated type of talent.
There are many myths surrounding data scientists, which can be counterproductive to hiring for the role.
The most common myth is the unicorn many organizations look for. This fictional character knows everything there is to know about data and is a coding superhero and a mathematical or statistical genius. Just point this individual at data, and magic will happen.
This false belief results in unrealistic job requirements and unrealistic expectations of what data scientists and data science can do.
Why hire a freelance data scientist?
Matt Johnson, former COO of data science consultancy Data Mettle, said there are three reasons clients tend to turn to freelance data scientists instead of hiring full-time help: They aren't sure they need a data scientist, they lack the expertise to understand what skills they need to hire or they just want to do a standalone project.
"Often, if they have some data and they think they can do something interesting or of value with it -- rather than hiring a data scientist -- it makes more sense to bring in someone for a few weeks or a month to explore the data, understand the business challenges and opportunities and what's feasible," Johnson said.
If a company doesn't understand data science, it's hard to hire for certain skills because hiring managers are unable to articulate what they need and why they need it.
"If they just want to do a standalone project, for example, they want a tool that optimizes scheduling for their workforce, [which will take] a month or two of work to build the tool, then they won't have much of a need for a full-time data scientist after that," Johnson said.
A freelance data scientist can help decision-makers understand some of the basics, including what a data scientist does, what a data scientist needs to be successful, and what data science can and cannot accomplish given the available data and other important factors organizations should consider.
What can go wrong with contract help
If a company hires a full-time data scientist, it's likely that no one will expect that person to produce results on day one. Before a data scientist can share any valuable insights, that individual must first understand what the business hopes to achieve, what data is available, what data isn't available, etc.
"The success of data science is completely predicated upon the data, and if your data is insufficient, incomplete or inaccurate, you're not going to get results -- or good results -- and the data scientist can't fix that because the data you have is the data you have," said Brandon Purcell, principal analyst at Forrester Research.
Robert O'CallaghanDirector of data science, Ordergroove
Nevertheless, unlike a new full-time data scientist, organizations often expect a freelance data scientist to be productive immediately just as with other types of contractors, and they struggle with getting results as quickly as desired.
"Even the most experienced data scientists face this problem as every company's data can be extremely different," said Robert O'Callaghan, director of data science at relationship commerce platform provider Ordergroove. O'Callaghan is also a former freelance data scientist.
Another misconception is that a freelance data scientist's project is complete once the analysis is finished, when implementation and maintenance are necessary for the company to extract business value from the data. For example, as new data comes in, a model must be tuned, or it will drift, becoming less accurate.
"I have seen multiple brilliantly analyzed -- and expensive -- projects fail to deliver value due to businesses believing that a project was complete before the back-end work was in place," O'Callaghan said.
It's also important to understand what should happen after the contract concludes. O'Callaghan explained that, in an ideal world, organizations would be able to plan ahead and give a detailed project to a freelance data scientist who would find specific insights that the organization would use for specific reasons.
"You can never really 100% anticipate your results, so you will need to be more flexible in understanding what the next step is once the work is complete," O'Callaghan said.
Fundamentally, companies typically do not scope freelance data science projects appropriately. And they may underestimate the impact these insights will have on business operations.
"You're going to be using that analysis to change the way you interact with customers, perform your operations or the way your human resources behave," Purcell said. "That's going to take longer than building a model."
If you don't already have a data science function, a freelance data scientist may help you better understand the opportunities and pitfalls. Freelancers are also a good choice for project work whether a data science function exists or not.
Be wary of making assumptions about what data science and data scientists can and can't do if you don't have the benefit of expert insight, however. Otherwise, your data science efforts and their results may fall short of expectations or fail entirely.