Data scientists are crucial parts of any organization, applying their unique skills to complicated challenges and messy datasets. If a data scientist does their job effectively, they’ll transform data into crucial strategic insights for a company. Over the next 10 years, as more companies rely on data science for a competitive edge, the demand for data scientists will only increase.
Although many data scientists work full-time for a single organization, many others are freelancers or contractors. The freelancing lifestyle includes a high degree of flexibility, allowing these data scientists to specialize in niche arenas, enhance their existing skills on their own schedule, and work with a variety of interesting clients. But what does it take to become a freelance data scientist?
Learn the Appropriate Data Scientist Skills
Before embarking on a life as a freelance data scientist, you must take several factors into consideration, including technical skills, business acumen, and soft skills (such as communication and empathy). According to Lightcast, which collects and analyzes millions of job postings from across the country, data scientist skills that pop up frequently include:
- Statistics (i.e., statistical analysis)
- Data processing
- Data visualization
- Data storage
- Programming languages (Python, R, and more)
- Machine learning
- Artificial intelligence
If you’re just beginning to explore a data science career (or you need a quick refresher on core data scientist principles), here are some online resources to help you out:
- Google—Machine Learning Crash Course
- CalTech: Learning from Data
- Codementor Data Science Tutorials and Insights
- KDNuggets Tutorials
- R-bloggers Tutorial: Data Science with SQL Server R Services
- Open Source Data Science Masters
- Simply Statistics
As we’ve covered before, many data scientists jump into the position from other roles, including (but certainly not limited to) economist, mathematician, software developer, and financial analyst. Many of these jobs utilize analytical and soft skills that transfer easily to a data science context.
Boost Your Profile to Attract Clients
There’s a lot of demand out there for data scientists, which means if you can demonstrate your competency, you have a good chance of landing solid contracts. “Paired with increasing demand for individuals who are well-versed in disciplines like artificial intelligence and machine learning, it’s not surprising that project-based data science is gaining in popularity,” says HackerRank CEO Vivek Ravisankar.
For data scientists who want to explore freelancing, it’s critical to have good communication and networking skills—those are table stakes for anyone looking to manage and build their own business. “I’d also say strong problem-solving skills are essential for any data scientist, and adaptability will also be important, as context might shift more frequently as an independent contractor,” Ravisankar adds.
Ryan Sutton, executive director for Robert Half's technology practice group, agrees that communications skills are critical for any freelance data scientist. Specialists in this profession must convey complicated concepts to a variety of stakeholders, including those without any kind of technical or data background, which means being able to explain your value proposition and methodology in plain English is important.
“You must be able to effectively and concisely communicate your concept, your findings, and integrate into that the client's feedback, because it's their home,” Sutton says. “You need to engage and listen to what they're telling you as you start to find some trends in that data.”
One great way to boost your profile as a data scientist is to get involved in competition websites such as Kaggle. “There are many skills a data scientist should continuously hone,” Ravisankar agrees. “When it comes to programming languages, proficiency in Python and SQL is essential, as these are the most widely used languages in data science. We’re seeing other languages, like R, are waning a bit in popularity. But it’s still dependent on the individual employer or project.”
Competency with data visualization tools such as Tableau is likewise critical, as you’ll use those to actually convey your findings to stakeholders. As machine learning and artificial intelligence (A.I.) gain momentum within organizations, you’ll also need at least some knowledge of building machine learning models via tools such as PyTorch.
As many companies rely on cloud platforms such as Amazon Web Services (AWS) and Azure for managing and storing data, familiarity with those platforms is likewise a must.
Once you’ve learned the necessary skills, you should integrate them into your data scientist resume, which will be a key part of your outreach for assignments. You should also update your online profiles and your professional website to reflect your new skills.
Building on Early Client Success
Sutton points out that diversity of engagement and diversity of environments can be a real boon to data scientists who crave a variety of challenges. “That IS your career,” he emphasizes. “That freedom and those expanded possibilities are definitely factors that attract a lot of data scientists.”
Kenneth Sardoni, senior VP for learning programs at CompTIA, who previously worked as a freelance data scientist, says that a company will often bring you in on a very specific problem, resulting in a two- or three-week engagement.
“What they want is the solution to the engagement, but they're also testing you to see how good your skills are,” he says. “I was hired by a large marketing company for that two-week engagement, and I did very well with it. Then I was invited to redesign their data warehouse—a 20-month project.”
If it's a new client, data scientists can expect a testing period where the employer will evaluate their work. “It's also not about the technology, at least in my experience,” Sardoni says. “They want to know if you understand the business and if you can identify the business requirements and then apply the technology to solve it.”
Once you come in and demonstrate your competency, your client may very well give you additional work and opportunities. If you do a good job, you can also rely on these clients as a reference as you build out your client base.
Sardoni also recommends attending data science conferences to make connections and learn how to improve your business: “Those are great opportunities for freelancers to network, especially if they can get on a panel for the conference or they can be a speaker for the conference and then they get people interested in them.”
Sutton notes he's seen some freelance data scientists become so enamored of clients that they wind up joining the company on a permanent basis. Otherwise, building up a solid roster of clients with repeat business is essential. As you gain clients, you may also feel compelled to further specialize in a particular aspect of data science. “They might decide to become an expert in web analytics and partner with small companies, focused on project-based roles where you come in and help a company in a particular area for a set number of months,” Sutton explains.
Staying Current, Planning Ahead
Ravisankar predicts the job market for freelance data scientists will see increased demand, especially those who specialize in machine learning and A.I.: "More businesses will be seeking project-based expertise, especially as the business world wakes up to all of the new possibilities unlocked by generative A.I.”
He advises those pursuing a career as a freelance data scientist to invest in continuous learning and skill development. “Stay updated on the latest tools, techniques, and industry trends to remain competitive in the market… Furthermore, build a strong professional network and most importantly — focus on delivering high-quality work.”
Sutton says going down the freelance path also requires a different mindset. You must think more like a businesses and understand you're not going to get 52 paychecks a year. “You really have to think that piece of it through, and make sure your lifestyle can support that transition,” he says. “Make sure you're making an intelligent decision that takes everything into account. You do have to factor in the cash flow of it all, so that you can set yourself up for success.”
Related Data Scientist Jobs Resources:
How To Become a Data Scientist
Data Scientist Interview Questions
