Does hiring a freelance data scientist make sense to you?

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, you might want to consider contract hiring. But a freelance data scientist isn’t always the answer to an organization’s needs.

Overall, companies are more likely 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 contract workers for a variety of reasons, particularly the rapid economic changes we’ve seen during the pandemic. of COVID-19. The report also found that 37% of organizations surveyed hired workers to access specialized skills, while 32% hired contracts to ease the burden on their full-time employees.

Of course, there are other options. Employers could hire a part-time data scientist or hire a consulting firm. If companies have a particularly compelling problem, like curing a form of cancer, another option is to run a data science competition using a platform like Kaggle. Some organizations even consult academic resources for cost-effective analysis.

However, before hiring data science talent, it’s best to understand what data scientists do, as there are some nuances specific to this type of freelancing that hiring managers would be wise to understand.

What is a data scientist?

A data scientist is a data scientist who often has an advanced degree in math or statistics and probably knows how to code in R or Python. The most sought-after data scientists also have relevant business expertise.

Although skills vary from person to person, the job of a data scientist is to help the employer solve difficult problems involving discovery, optimization and/or prediction. The role can be considered part of IT, or it can be specific to a departmental function. Of all the possible data-related roles, data scientists tend to be the most sophisticated type of talent.

Many myths surround data scientists, which can be counterproductive to hiring for this position.

The most common myth is the unicorn that many organizations are looking for. This fictional character knows all there is to know about data and is a programming superhero and math or statistics whiz. Just direct that individual to the data, and the magic will happen.

This false belief translates into unrealistic job demands 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. time: they’re not sure they need a data scientist, they don’t have the expertise to understand what skills they need to hire, or they just want to do a standalone project.

“Often if they have data and they think they can do something interesting or valuable with it – rather than hiring a data scientist – it makes more sense to bring someone in for a while. a few weeks or a month to explore the data, understand the business challenges and opportunities and what is doable,” 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.

“If they just want to do a standalone project, for example, they want a tool that optimizes their workforce planning, [which will take] a month or two of work building the tool, then they won’t really need 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 available data and other important factors that organizations should consider. .

What Can Go Wrong With Contract Aid

If a company hires a full-time data scientist, chances are no one will expect that person to deliver results from day one. Before a data scientist can share valuable information, that person must first understand what the business hopes to achieve, what data is available, what data is not available, etc.

“Success in data science is all about data, and if your data is insufficient, incomplete, or inaccurate, you won’t get results – or good results – and the data scientist can’t solve that problem because the data you have is the data you have,” said Brandon Purcell, principal analyst at Forrester Research.

Even the most experienced data scientists face this problem because each company’s data can be extremely different.

Robert O’CallaghanDirector of Data Science, Ordergroove

However, unlike a new full-time data scientist, organizations often expect a freelance data scientist to be immediately productive, much like other types of contractors, and they struggle to get results as well. quickly as desired.

“Even the most experienced data scientists struggle with this problem, because each company’s data can be wildly 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 an independent data scientist’s project is complete once the analysis is complete, when implementation and maintenance are necessary for the business to extract business value from the data. For example, as new data comes in, a model needs to be adjusted or it will drift and become less accurate.

“I’ve seen several brilliantly analyzed — and expensive — projects fail to deliver value because companies thought a project was done before the back-end work was in place,” O’Callaghan said.

It is also important to understand what should happen after the conclusion of the contract. O’Callaghan explained that, in an ideal world, organizations would be able to plan ahead and assign a detailed project to an independent data scientist who would find specific information that the organization would use for specific reasons.

“You can never really 100% anticipate your results, so you’ll need to be more flexible in understanding what the next step is after the job is done,” O’Callaghan said.

Basically, companies generally don’t appropriately define freelance data science projects. And they may underestimate the impact this information will have on business operations.

“You’re going to use this analytics to change the way you interact with customers, perform your operations, or the way your human resources behave,” Purcell said. “It’s going to take longer than building a model.”

At the end of the line

If you don’t already have a data science function, an independent data scientist can help you better understand the opportunities and pitfalls. Freelancers are also a good choice for project work, whether or not a data science function exists.

However, beware of assumptions about what data science and data scientists can and cannot do without the benefit of expert knowledge. Otherwise, your data science efforts and results may fall short of expectations or fail altogether.

Sean N. Ayres