How To Become a Highly Paid Freelance Data Scientist in 2024

Over 30% of the American workforce was freelance amid the pandemic. While some of this was driven by the job losses during the initial days, much of it came from experienced professionals resigning from full-time positions to go freelance. This trend that’s being called the great resignation is becoming increasingly common in the tech industry.

On the other hand, businesses are much more willing than ever before to hire freelancers. Over 70% of hiring managers say they are planning to increase their use of freelancers over the next six months. This has led to rapid growth in platforms like Upwork, Fiverr, etc., so much so that LinkedIn wants a piece of the pie.

As a high-technology industry with a significant talent gap, data science is embracing freelancers wholeheartedly. In this blog post, we discuss what opportunities there are, how you can 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 considerations. There is a huge supply gap in data science and thousands of opportunities are available. So, a qualified freelance data scientist is likely to have multiple options to choose from. Given that freelance data science gigs are typically for professionals with at least 2-3 years of experience, you can also demonstrate your value and command high pay.

On the other hand, being a freelancer is like running a small business. You will be your own boss, yes, but you will also be your own salesperson, project manager, marketer, accountant, admin, etc. This brings with it additional work and more demand on your time.

However, most freelancers figure out the balance between the data science and administrative aspects of their career soon. With experience, it becomes easy to be a successful freelancer in data science.

What Are the Pros of Becoming a Freelance Data Scientist?

Across multiple dimensions, there are several reasons for you to consider becoming a freelance data scientist. Let’s look at them one by one.

Time Flexibility

As a freelancer, you are not restricted to the 9-5 schedule. You can choose when you want to work during the day and throughout the year. You can block off large chunks of the year to pursue other ambitions, take breaks, travel, or whatever else you’re excited about.

Income Flexibility

Freelancers decide their rates. For instance, you might only have 2 years of full-time work experience but have been working on data science projects for much longer than that. As a freelancer, you can name your price based on the value you bring, without the constraints of traditional payscales that go by your years of experience.

Location Independence

As a freelance data scientist, you can work from anywhere. Some choose idyllic locations in south Asia. But even if you don’t go that far, you can work from cafes, friends’ houses, holiday homes, co-working spaces, or wherever else you get the inspiration. Creating work around your lifestyle and not being confined to a specific geographic location is what makes freelancing all the more exciting!

Career Growth

A freelancer’s career growth is often non-linear. You can take up varied projects, accumulate wide-ranging experience, gaining skills more rapidly than a single full-time job can offer. You can become a generalist or a super-specialist—having complete control of your career trajectory.

What Are the Cons of Working as a Freelance Data Scientist?

Freelancing can be exciting. But remember that it is not just a new way to do work, but a completely different lifestyle. Here’s what you need to carefully consider before jumping to freelance.

Unstable Income

While your pay is flexible, it is also unstable. As a freelancer, it is your responsibility to look for new clients/projects. Without an ongoing sales effort, you might find yourself unemployed quickly. Moreover, you will need to negotiate your pay with every new client, making it an effort to retain your rates.

Complicated Taxes

Being a freelancer is just like being a small business owner, so self-employment taxes apply. You can claim expenses, but that means you must make an additional effort into maintaining bills, separating business and personal expenses, etc. Also, as a freelancer, you might have to pay taxes on a quarterly basis, instead of yearly.

No Employee Benefits

Your clients are under no obligation to provide you with any benefits. So, you need to make your own arrangements for vacation pay, 401K, health insurance, etc. Whereas a full-time data scientist employee is in much safer place when it comes to employee benefit. You might also need a backup plan for when you are sick or during personal emergencies.

Limited Interactions

Some say that freelancing is lonely. Clients will have limited interactions, almost always sticking to business alone. Without colleagues and teams to collaborate with, you might feel isolated. And this is known to lead to stress, anxiety, and demotivation.

How To Become a Freelance Data Scientist

While freelancing is a promising career path, it is not perfect. Before making the jump, carefully consider its pros and cons. Evaluate your personal characteristics like risk appetite, financial acumen, need for social interaction, etc. to identify the struggles you might have and build support systems for them. For instance, you can get the help of a tax consultant to take care of compliance. You can deliberately build your social circles to compensate for the lack of interaction at work. Be deliberate about your unique needs and situations.

If you’ve decided to take the plunge, here’s how to become a freelance data scientist.

1. Complete a Course

The best way to get started in freelance to complete a data science bootcamp. You’ll gain practical insights, network with established engineers, and even have the option of taking on a part-time or full-time job in the industry.

2.Build an Online Presence

To be hired as a freelance data scientist, you need to be visible. So, the first and most crucial step is to build a strong online presence. Typically, freelancers create their own websites to do this. But it is helpful to have profiles on networks like Twitter, LinkedIn, GitHub, etc.

• Present your skills and experience clearly, including the programming languages you know, tools you’re comfortable with, etc.
• Have a portfolio section to demonstrate your skills and experience
• You can also write case studies to demonstrate your approach to problem-solving, working style, etc.
• If you have a roster of recognizable companies as clients, include their names (with their permission)
• Update your LinkedIn profile on a regular basis

3. Pick a Niche

When organizations look for freelance data scientists, they want them to hit the ground running immediately. This means that organizations expect freelancers to come with an understanding of the industry, market, and business landscape in addition to data science skills. For instance, if a financial services company is looking for a freelance data scientist, they would need you to have some experience in banking, stockbroking, etc.

So, pick a niche. This needn’t be just an industry. This could 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 way, you can differentiate yourself and command better pay.

4. Refine Your Skills

If you’re thinking about starting a career as a freelance data scientist, you probably already have the basic skills the job needs. So, begin to refine them. Take your skills up a notch, becoming an expert in what you do. Apart from statistical knowledge, programming skills, data science tools, and visualization skills, develop interpersonal skills as well in sales, presentation, negotiation, project management, communication, etc.

5. Set Expectations

One of the biggest reasons freelancers get frustrated is scope creep—where the project’s requirements change or increase typically resulting from misunderstanding or unclear communication. So, before you take on any project, set clear expectations about what you will do, what that will look like, what it will achieve, etc. Here are a few 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

6. Set Your Rates

Typically in the US, freelancers charge by the hour. This could be anywhere between $45-200 per hour depending on your educational qualifications, experience, niche, skills, demand, availability, etc. You can also charge per project. Depending on the scope of work, the time you’ll spend, and the value you’ll generate, you can charge a lump sum. Before deciding on the rates, consider two things.

Your Needs and Expectations

How much money do you need to live the lifestyle that you want? The answer to this question will help you decide how much work you need to take on and how much you should charge.

Market Rates

What 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 find out market rates from websites like Glassdoor, which collects data from the freelance data scientist community itself. You can also look at websites like UpWork, Freelancer, Fiverr, etc. to see what is being offered to other freelancers.

7. Find Freelance Data Scientist Jobs

With all that done, you’re ready to take on real work. Freelancers find work through one of the three following channels.

Personal Networks

Whether it’s their past colleagues, collegemates, or their personal networks, freelancers get their first break typically from people they already know. So, once you’ve decided to go freelance, make an effort to let your friends and colleagues know. Request them to refer you if they come across a suitable project. Write about topics that you feel strongly about—this could be on your own blog, Medium, etc. Participate in data science events and conferences.

Job Boards

Just like full-time work, there are job boards for freelance data scientist jobs as well. Portals like LinkedIn also have freelance opportunities listed. There are also freelance-specific platforms like Upwork, freelancer, Toptal, Fiverr, etc., where clients advertise their needs. Startup communities like Cofounders Lab and AngelList advertise openings in startups. Tech communities like the Data Science Stack Exchange or Lemon.io can also offer the support and interactions you need.

Social Media

The other channel you must not skip is social media. There are dozens of posts every day on channels like Twitter, LinkedIn, etc. where organizations seek freelance data scientists. Even if you don’t directly follow the organization/manager in question, you can be referred by those who do. So, make sure you’re present in all relevant social media, following the right people, and active in your field of work.

8. Collect Testimonials and Referrals

There is nothing that establishes you as a credible freelance data scientist as an endorsement or testimonial from a client can. At the end of every project, schedule a review meeting and collect feedback. Request them for testimonials that you can use on your website, presentations, etc.

How To Be Successful as a Freelance Data Scientist

Success as a freelance data scientist depends heavily on your ability to execute complex projects seamlessly. This needs a laser-sharp approach to your work as well as overall professionalism in your delivery. To achieve that,

Only Pursue Projects That Match Your Skills

It is important for a freelancer’s credibility to deliver what they’ve promised. While it can be tempting to take on new challenges, make sure you can deliver them without a glitch. If you’re relying on another freelancer or a partner to complete the project, be realistic and transparent about it.

Set Goals and Expectations

Make sure that your client expects to receive what you are going to deliver. It’s not uncommon for clients and freelancers to have a completely different understanding of the project—make sure you’re both on the same page. Once the project has begun, communicate regularly about the progress you’re making. It’s better to be proactive than wait for the follow-up.

Build a Routine

One of the biggest complaints among freelancers is that they get so engrossed in their work that they lose track of time. Without a conscious effort, you might end up spending too much time on work unnecessarily. To avoid that:

• Build a routine
• Set a start and stop time for your workday
• Block off your weekends for personal activities
• Include adequate breaks and exercise time
• Schedule yearly vacation and inform clients in advance

Invest in an Office

It is tempting to lie on the couch and do all the work, but that will soon become unsustainable. Get yourself a comfortable table and chair to avoid back pain. If you need wide-screen monitors, a keyboard and mouse, etc. for your work, invest in them. If possible, separate your workspace from your living space. This will stop work from overtaking all areas of your life.

Freelance Data Scientist Success Stories

If you still feel on the fence, here are the stories of a few freelance data scientists for inspiration.

Freelancing To Stay Creative

“When you’re internal and in one job, you naturally get a group-think happening. Then, it becomes difficult to think outside the box and solve problems. I like to look outside-in because I can place myself in multiple perspectives and find the right solution,” says Kat Campise, a freelance data scientist on UpWork. In this video, she discusses her journey as a freelancer and what she counts as success.

Setting Yourself Up for Freelance Success

In his video, Ken Jee shares how he landed his first client as a freelancer through the old-fashioned method of networking. His advice to freelancers is to focus on building a good portfolio, networking, specializing, problem-solving, and being diligent with delivery and follow-ups.

Seeking and Leveraging Mentorship

Springboard Alum Haotian Wu believes that mentorship is enriching to one’s learning experience. “My mentor’s answers were incredibly detailed and gave me the mathematical background on topics that I found very helpful. As someone with a more technical background, my mentor calls ended up being more like discussions,” he says.

Kickstart Your Data Science Career Today

Whether you’re looking to be a freelancer or not, to kickstart a data science career, you need strong foundational skills and hands-on experience. Springboard’s Data Science Bootcamp offers you all this and more.

• This program’s 500+ hour curriculum covers the gamut of skills you need
• It includes two capstone projects, in addition to multiple smaller projects to get you comfortable working with data science
• Your mentor will guide you through every step of the way
• Your career coaches will ensure a smooth transition to a job

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!