More than 30% of the American workforce was self-employed amid the pandemic. While some of this was due to early job losses, much of it came from experienced professionals who left full-time jobs to become self-employed. Dubbed the "big quit," this trend is becoming more prevalent in the tech industry.
On the other hand, companies are much more willing than ever to hire freelancers. More than 70% of hiring managers say they plan to use more freelancers in the next six months. This has led to rapid growth on platforms like Upwork, Fiverr, etc., so LinkedIn wants a piece of the pie.
As a high-tech industry with a significant talent shortage, data science wholeheartedly welcomes freelancers. In this blog post, we discuss the opportunities out there, how to find them, and what you need to do to become a freelance data scientist.
Is It Easy To Become a Freelance Data Scientist?
Yes, but with consideration. There is a huge gap in the data science offering and thousands of opportunities are available. As such, a qualified independent data scientist will likely have several options to choose from. Since freelance data science jobs are usually for professionals with at least 2-3 years of experience, you can also prove your worth and earn a great salary.
On the other hand, being self-employed is like running a small business. Yes, you will be your own boss, but you will also be your own salesperson, project manager, marketer, accountant, administrator, etc. This involves additional effort and a greater expenditure of time.
However, most freelancers quickly find the balance between data science and the administrative aspects of their careers. With experience, it becomes easy to be a successful data science freelancer.
What Are the Pros of Becoming a Freelance Data Scientist?
Across multiple dimensions, there are multiple reasons to consider becoming an independent data scientist. Let's look at them one by one.
As a freelancer you are not limited to 9-5 hours, you can choose when you want to work during the day and all year round. You can use a large part of the year for other ambitions, breaks, trips or whatever inspires you.
Freelancers set their prices. For example, maybe you only have 2 years of full-time work experience, but you've been working on data science projects for much longer. As a freelancer, you can set your price based on your added value, without the limitations of traditional pay scales that depend on your years of experience.
As a freelance data scientist, you can work from anywhere. Some choose idyllic locations in Southeast Asia. But even if you don't go that far, you can work in cafes, at friends' houses, in vacation homes, in co-working spaces or wherever you find inspiration. What makes freelancing even more exciting is creating work that fits your lifestyle and isn't tied to a specific geographic location!
The professional development of a freelancer is often not linear. You can take on a variety of projects, gain a wealth of experience, and acquire skills faster than would be possible with a single full-time job. You can become a generalist or a super specialist and have full control over your career path.
What are the disadvantages of working as a freelance data scientist?
Being independent can be exciting. But remember, it's not just a new way of working, it's a completely different way of life. Here are some things you should carefully consider before starting your own business.
Your salary, although flexible, is also unstable. As a freelancer, it is your responsibility to seek out new clients/projects. Without a continuous sales effort, you could quickly find yourself out of a job. In addition, you must negotiate your salary with each new client and strive to honor your rates.
Being self-employed is like owning a small business, so self-employment taxes apply. You can claim expenses, but that means you have to worry about keeping your bills, separating business and personal expenses, etc. Also, as a self-employed person, you may have to pay taxes quarterly rather than annually.
Your customers are under no obligation to grant you benefits. Therefore, you must make your own arrangements for vacation pay, 401Ks, health insurance, etc. Whereas a full-time employee of a data scientist is much more secure when it comes to benefits. You may also need a back-up plan in case of illness or personal emergency.
Some say it's loneliness to be alone. Customers will have limited interactions and will almost always be business-only. Without colleagues or teams to work with, you can feel isolated. And this is known to lead to stress, anxiety and demotivation.
How To Become a Freelance Data Scientist
- Build an Online Presence
- Pick a Niche
- Refine Your Skills
- Set Expectations
- Set Your Rates
- Find Freelance Data Scientist Jobs
- Collect Testimonials and Referrals
Although freelancing is a promising career path, it's not perfect. Before you start, weigh the pros and cons carefully. Assess your personal characteristics such as risk tolerance, financial knowledge, need for social interaction, etc. to identify potential difficulties and create support systems for them. For example, you can hire the help of a tax compliance advisor. You can deliberately create your social circles to compensate for the lack of interaction in the workplace. Be aware of your individual needs and situations.
If you've decided to take the plunge, here's how to become a freelance data scientist.
1. Build an Online Presence
To get hired as a freelance data scientist, you need to be visible. So the first and most important step is to establish a strong online presence. Freelancers usually create their own websites for this purpose. However, it is useful to have profiles on networks like Twitter, LinkedIn, GitHub, etc.
- Clearly present your skills and experience, including programming languages you know, tools you know, etc.
- Create a portfolio section to showcase your skills and experience
- You can also write case studies to demonstrate your problem-solving approach, working style, etc.
If you have a list of recognizable customers, please provide their names (with their permission). Update your LinkedIn profile regularly
2. Pick a Niche
When companies are looking for freelance data scientists, they want them to get started right away. This means companies expect freelancers to have an understanding of the industry, market, and business landscape, in addition to data science skills. For example, if a financial services company is looking for a freelance data scientist, they will need a background in banking, stock trading, etc.
So choose a niche. It doesn't have to be just one industry. It can also be a specific business process like fraud detection or compliance, or a use case like a recommendation engine. With experience, you can also create your own proprietary processes and systems to ensure high quality. This allows you to differentiate yourself and earn better salaries.
3. Refine Your Skills
If you're considering starting a career as a freelance data scientist, chances are you already have the basic skills required for the job. So start tweaking them. Sharpen your skills and become an expert in what you do. In addition to statistical knowledge, programming skills, data science tools and visualization skills, develop interpersonal skills in sales, presentation, negotiation, project management, communication, etc.
4. Set Expectations
One of the biggest causes of frustration for freelancers is project scope growth, where project requirements change or increase, usually due to misunderstandings or unclear communication. So before starting a project, set clear expectations about what you will do, what it will look like, what you will achieve, etc. Here are some things to include:
- Scope of work
- Timelines of delivery
- Inclusions and exclusions
- Post-delivery support
- Your availability
- Preferred means of communication
- Expected response time
- Necessary reviews and sign-offs
- Payment schedule
5. Set Your Rates
In the United States, freelancers are generally paid by the hour. It can range from $45 to $200 per hour depending on education level, experience, niche, skills, demand, availability, etc. You can also charge per project. You may be able to charge a flat rate depending on the amount of work, time involved and value added. Before deciding on rates, you should keep two things in mind.
Your Needs and Expectations
How much money do you need to live the lifestyle you want? The answer to this question will help you decide how much work you need to undertake and how much to charge.
How much are organizations willing to pay someone with your experience and skills? The answer to this question will help you position yourself competitively in the market. You can learn more about market prices on sites like Glassdoor, which collect data from their own community of independent data scientists. You can also check out sites like UpWork, Freelancer, Fiverr, etc. to see what's out there for other freelancers.
6. Find Freelance Data Scientist Jobs
With all that done, you're ready to do the real work. Freelancers find work through one of three channels.
Whether it's former co-workers, college roommates, or their personal networks, freelancers often find their first breakthrough with people they already know. So, if you have decided to start your own business, you should make an effort to let your friends and colleagues know about it. Ask them to recommend you if they find a suitable project. Write about topics that particularly interest you. This can be on your own blog, on Medium, etc. Attend data science events and conferences.
In addition to full-time jobs, there are also job boards for freelance data science jobs. Jobs for freelancers are also listed on portals such as LinkedIn. There are also dedicated freelance platforms like Upwork, Freelancer, Toptal, Fiverr, etc. where customers register their needs. Startup communities such as Cofounders Lab and AngelList post open startup positions. Tech communities like Data Science Stack Exchange or Lemon.io can also provide the support and interaction you need.
The other channel you shouldn't miss is social media. There are dozens of posts every day on channels like Twitter, LinkedIn, etc. This is where companies look for freelance data scientists. Even if you don't directly follow the organization/manager in question, you can be recommended by those who do. So make sure you are on all relevant social media, follow the right people, and be active in your field of work.
7. Collect Testimonials and Referrals
There is nothing that will establish you as a credible independent data scientist like a customer testimonial or testimonial. Schedule a debrief meeting at the end of each project and collect feedback. Ask them for testimonials to use on your website, presentations, etc.
How much can a data scientist earn if he is working only on freelance?
Some freelance data scientists charge less than $30 per hour; others charge more than ten times. There is a wide range of people who call themselves data scientists, and this is reflected in the different hourly rates. Therefore, it is impossible to give a meaningful number without giving many more details about the data scientist in question.
A useful rule of thumb for all freelancers is that you will be paid twice the hourly rate you would be paid if you were working full time. Since there are about 2,000 hours worked per year, it's like dividing your annual salary by 1,000 to get your freelancing hourly rate.
For example, if you could earn $100,000 per year for a full-time job, your freelance rate would be $100 per hour. This is of course only a general rule. And the fees you charge can affect the amount of work you can get.
Either way, if you're a data scientist, consider this a data science problem! Gather public job data from people with similar qualifications to yours and see what they tell you.