A Brief Guide to Freelancing as a Data Scientist

Data scientists have become some of the most sought-after professionals across practically every major industry right now, driving demand and salary packages to new heights. Our correspondent Milos Nikolic highlights how data science graduates can earn a base salary of $95,000, garnering them the top spot in the list of highest-paying entry-level jobs last year. This lucrative career path provides data scientists’ significant leverage when negotiating their position and salary, too.

Some take the already appealing data science career further by starting their own practice as a freelancer. Freelancing means you not only have control over your time, but you also have the power to choose which projects to take on. This is why it’s not surprising that freelancing is becoming a viable option for a large number of the population, regardless of profession. In fact, a whopping 16.5 million Americans are currently part of the gig economy and work part-time or as freelancers. For data scientists, great pay and this unique flexibility create the ideal combination for a successful career.

Whether you’re a seasoned expert or just starting out in the field, there are several ways you can make the switch to freelancing. Here, we will discuss some essential tips to keep in mind when starting out as a freelance data scientist.

Identify Your Skills

The gig economy is a highly competitive market, with experienced freelancers showcasing their long list of skills and extensive portfolios. As a data scientist, it’s particularly important to stand out in a competitive talent pool. The best way to do this is by matching your skills with ones that are currently in huge demand.

Tech Republic lists programming languages like Python and SQL as some key skills you can focus on, along with exciting innovations like machine learning. But if you haven’t mastered these skills yet, the good news is that it’s easy to beef up your resume with the myriad of resources available online. Udemy has a variety of highly-rated data science courses on topics such as machine learning, Python, and business analysis. Investing a few minutes every day to learn new skills can lead to long-term success in your budding freelancing career.

Find Your Specialization

While it’s good to master the top skills that all data scientists possess, you will eventually have to find a specialization that really sets you apart in the market. Although it’s not a requirement, focusing on a niche means you’ll build a loyal client base who know you for your unique skills.

These skills can be specific to the projects you enjoy doing or markets you work with the most. For instance, you can specialize in fraud detection for insurance companies or focus on predictive analytics in retail. As long as you can back these up with actual job experience or credentials from courses, you’ll have no problem landing clients who will keep coming back — as well as any referrals they will send your way.

Be Smart About Platforms

Having your freelancing profile up on platforms like Upwork, Fiverr, and LinkedIn is just the first step in putting yourself out there. Remember that you also have to maximize the best platforms that cater to your potential clients.

It’s important to note that LinkedIn had the most job postings related to general and technology-specific data science positions in 2018, followed closely by Indeed and SimplyHired. Prioritize these platforms that are known for hiring in the data science field, and make sure to constantly update your profile with your latest projects and recommendations from clients.

All in all, it’s a good time to freelance as a data scientist given the high demand and above-average pay. Set yourself up for success in the field by covering all the required basic skills and mastering ones that cater to a niche audience. Before you know it, you’ll have the ideal job with flexible working hours, a solid client base, and projects that you enjoy working on.

Disclaimer: The information on this site is provided for discussion purposes only, and should not be misconstrued as investment advice. Under no circumstances does this information represent a recommendation to buy or sell securities.

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