Guide to starting a career as a freelance data scientist
India’s freelance market grew by 46% in 2020 as a result of the pandemic. According to a Payoneer report, India has the second fastest growing freelancer stack with 160% year-over-year revenue growth.
Companies around the world are turning to remote working or gig-based models. As a result, the indie ecosystem is booming, especially the IT sector. The ripple effect is also felt in the data science ecosystem. Below, we look at the pros and cons of working as a freelance data scientist.
- Selective work: You can be your own boss and choose the projects that interest you.
- Master of your time: Being a freelance data scientist means you can set your working hours and schedule tasks. Flexibility is priceless.
- Versatility: Freelancers gain a broader perspective by working on different projects, teams, and getting their hands dirty with exciting projects.
- No perks and benefits: Freelancers don’t get perks and benefits like full-time employees like health insurance, PF, etc.
- Administrative work: Besides the basic work, the freelancer must also take care of the administrative and financial side.
- Lack of job security: The full-time employee has a stable income. Freelancers might not land projects consistently.
How to become a freelance data scientist
Freelance data scientists have the opportunity to work on interesting projects and with different teams. Today, a diploma or a certificate is not enough to land independent projects.
- Develop a portfolio
“Understanding the field can take some time, and getting started early means you’re well prepared when you start your career,” said Shobhit Nigam, data science consultant and data science lead at KnowledgeHut. However, before taking the plunge, one must first work on some projects, acquire knowledge in the field and participate in technical forums and build credibility on the street. GitHub is the go-to platform that meets all these requirements.
- Search for gigs/projects on freelance portals
Devesh Mishra, a freelance data scientist and expert on Kaggle notebooks, said that he has created profiles on various freelance portals like Upwork, Fiverr, Guru.com, freelancer, etc. However, creating a portfolio and bidding for projects does not guarantee a project. Therefore, freelance data scientists should make their profiles eye-catching and organize the information so that any potential client visiting the profile can get a quick glimpse of their skills and experience. Having a website also goes a long way in attracting customers.
- Contact companies that need data scientists
Many companies keep freelancers under contract. To get long-term clients, freelance data scientists need to reach out to tech companies, startups, and enterprises. Down payments are a good fallback option if projects become scarce.
Freelance data scientist Krishna Sai Vootla said joining Toptal gave him the confidence and stability to become a full-time freelance data scientist. Platforms like Toptal, Flexiple, Hubbstaff Talent, etc. provide freelancers with a steady stream of projects. “Having good private insurance that covers your family and health issues goes a long way toward becoming a freelance data scientist. It also helps if you have some savings before venturing into the freelance world,” he added.
India has the third largest ecosystem of startups in the world, with more than 69,000 recognized by the Department of Promotion of Industry and Internal Trade. Companies seek outside help or outsource their projects. In other words, the opportunity is there for freelancers.
Before switching, freelancers should keep a few things in mind:
- Structure your process: Customers are impressed if you have a game plan. Create models for different types of AI/ML projects.
- Find your niche: Most freelance data science projects are specialized jobs. Therefore, it is important to find your strength and develop it. Develop a niche skill.
- Full-time or part-time: It’s best to work on a few side projects and build a portfolio before going full-time freelancing. However, the downside is that the split focus can affect the employee’s performance.