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Guide to starting a career as a freelance data scientist

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Guide to starting a career as a freelance data scientist
Guide to starting a career as a freelance data scientist

Platforms like Toptal, Flexiple and Hubbstaff Talent. They provide freelancers with a steady stream of projects.
Companies around the world are shifting to remote work or gig-based models. As a result, the freelance ecosystem is thriving, especially in the IT sector. The side effect is also being felt in the data science ecosystem. Below, we’ll look at the pros and cons of working as a freelance data scientist.

Advantage
Selective work: You can become your own boss and choose the project that interests you.
Manage your time: Being a freelance data scientist means being able to set work hours and schedule tasks. Flexibility is valuable.
Diversity. Freelancers can gain a broader perspective by working on different projects and teams and enjoying interesting projects.
Flaws
No Benefits: Self-employed people cannot receive employee benefits such as health insurance and FP.
Administrative Responsibilities: In addition to the main task, freelancers also need to deal with administrative and financial aspects.
Insecurity of employment: the income of permanent employees is stable. Freelancers may not be able to consistently land a project.
How to Become a Freelance Data Scientist
Freelance data scientists have the opportunity to work on interesting projects and teams. Degrees and certificates alone are not enough today to implement an independent project.

Develop portfolio
“It can take some time to understand the domain. Getting started early means you’re well-prepared to start your career,” says Shobhit Nigam, data science and data science consultant at Knowledge Hut. However, before you take the plunge, you need to work on a few projects first, gain domain knowledge, participate in tech forums, and earn the trust of the streets. GitHub is the access platform that meets all of these requirements.

Concert/project explorer on an independent portal
Devesh Mishra, a freelance data scientist and Kaggle notebook expert, says he has profiled on various freelance portals such as Upwork, Fiverr, Guru.com and Freelancer. However, creating a portfolio and bidding on a project does not guarantee a project. Therefore, freelance data scientists make profiles compelling, organize information, and allow potential clients to access the profile to get a general idea of ​​their skills and experience. Having a website can also be a great help in attracting customers.

Contact companies that need data scientists
Many companies keep freelancers on a salary basis. To get long-term clients, freelance data scientists need to approach tech companies, startups, and companies. If you run out of projects, royalties are a great alternative.

Krishna Sai Wutla, a freelance data scientist, said joining Toptal gave her the confidence and stability to become a full-time freelance data scientist. Platforms like Toptal, Flexiple and Hubbstaff Talent. They provide freelancers with a steady stream of projects. “Getting good private insurance to cover family and health issues can be very helpful for becoming an independent data scientist, even if you have some savings before you enter the freelancing world. It helps,” he added.

India has the third largest startup ecosystem in the world with over 69,000 recognized by the Department of Domestic Trade and Industrial Development. The company either receives external support or outsources the project. In other words, freelancers have a chance.

Freelancers need to keep a few things in mind before switching.

Build a process: If you have a game plan, your customers will be impressed. Create templates for different types of AI/ML projects.
Find your niche. Most freelance data science projects are professional work. Therefore, it is important to find your area of ​​expertise and build on it. Develop niche skills.
Full-time or part-time: It’s best to work on some side projects and build a portfolio before moving on to full-time freelancing. However, the downside is that a split approach can impact employee productivity.

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