How to Become a Successful Data Science Freelancer in 2022
In this article, I will walk you through how you can use your data science skills to land freelance gigs.
Photo by Andrew Neel on Unsplash
For most people, having a freelance career is a dream come true.
Freelancing provides you with the ability to work from home. There is no need to clock in and out on a daily basis. You can handpick tasks to work on, and reject jobs that you don’t find interesting. You have the freedom to work at your own pace and can choose to take occasional breaks to unwind.
If you are an aspiring data scientist, the good news is that a full-time job isn’t your only career option. There are many companies and individuals out there who engage data scientists on a freelance basis.
In this article, I will walk you through how you can use your data science skills to land freelance gigs. I will cover the following topics:
- The pros and cons of becoming a freelance data scientist
- The types of freelance data science jobs you can take up
- How to land your first freelance gig
- How to build a strong portfolio that attracts multiple job opportunities
Pros and cons of freelancing for data science
Having a freelance career in any field provides you with the freedom to work wherever and whenever you want. You get to save a lot on expenses like petrol and food since you don’t need to travel to the office everyday. If you find that you’re working on repetitive and boring tasks, you have the opportunity to switch things up and only accept projects that excite you.
If you work full-time for one company, you are restricted to the scope of work within the company. You don’t get to work on tasks in different domains.
If you freelance, however, you gain exposure to a variety of industries. This looks great on your portfolio, and qualifies you to apply for a wider range of jobs in the future.
However, a freelance career also comes with its own set of disadvantages.
Firstly, you will lack job security. There aren’t many employers hiring data scientists on a freelance basis.
It is generally individuals or smaller companies/startups that choose to hire freelancers. They generally don’t require data scientists, and don’t have a proper data pipeline in place.
Larger companies that do require data scientists generally have fixed positions. They would prefer to hire employees on a full-time basis, and almost never hire freelancers.
This is why you will see a lot more freelance job openings for web developers, writers, and designers, and only a handful of opportunities for data scientists.
The biggest disadvantage of becoming a freelance data scientist is that you will need to constantly source for jobs in a space where there aren’t many, and where supply tends to exceed demand.
This being said, however, you can get creative with the services you offer as a freelance data scientist. You don’t need to limit yourself to building machine learning models for organizations. There are many other areas that intersect with the skillset of a data scientist that you can take advantage of to land more freelance opportunities.
Here are a few of them, based on freelance jobs I’ve taken up in the past:
This is a task that many small organizations and individuals hire data professionals to perform.
I’ve worked with a person who wanted to collect five years worth of social media data in order to understand trends in user sentiment about certain stocks. This would help him improve an existing model that predicted which stock to invest in.
Many companies also require large amounts of data to aid with decision-making. I’ve scraped pricing information, user review data, social media posts, and job listings in the past to help organizations come up with key decisions around product offering and brand positioning.
This is one of the most common tasks I perform to help organizations decide on how to position their brand, their target audience, and the kind of ads to run.
While there is a lot of emphasis placed on model building in data science, most small to mid-sized organizations don’t have enough data available to do this, especially during their initial stages.
These companies rely on external data that can then be analyzed to identify trends. These patterns can then tell you who these companies should be targeting, the types of products that should be promoted, when these products should be released, and how they should be advertised to different people.
There are many technical publications and learning platforms out there that require data scientists to write articles around the subject. This includes opinion pieces, tips, and tutorials on data science.
Many of these platforms need a person who is able to break down highly technical subjects down in a manner that can easily be digested by beginners.
A great example is DataCamp’s blog. They have tutorials on almost every topic in data science. Their tutorials are very easy to understand, even if you are a beginner in the field. Every step is clearly outlined, and the code examples always include an explanation as to why things are being done a certain way.
This makes it very easy for the end-user to follow along, since they aren’t blindly copy pasting code that they don’t understand. They actually gain knowledge of the topic by the time they complete the tutorial, and will definitely go back to the same site the next time they want to learn something.
Breaking something highly technical down into a simple format isn’t an easy task. If you can work on this skill, there are many opportunities for you in the data science blogging space.
If you want to become a data science writer but aren’t sure how, you can read this article to get started.
After learning data science topics, you can conduct workshops and training sessions to break them down for beginners in the industry.
Many online data science learning platforms are looking for instructors to cover specific topics, and they will engage you on a one-off or contractual basis.
You can hold live sessions for students, or create recorded videos which will be uploaded to the platform and can be accessed by anyone.
The roles I’ve listed above are based on the freelance tasks I’ve taken on in the past. Apart from the above, I’ve also built machine learning models for companies on a one-off basis. This was a more complicated task than I expected, since these organizations are pretty small and their data isn’t cleaned or stored properly.
I had to spend a lot of time figuring out the relationships between different tables, how database access could be automated so the model could run each time, and cleaning the data so it could easily be queried.
I’ve spent a lot of time just dealing with the data and trying to understand it, and far less effort was spent on model building.
While these projects took up a huge portion of my time, I was able to learn a lot, and also picked up on many different tools that could be used for handling large amounts of data.
How to land freelance data science jobs?
Now that you know the types of tasks you can take up as a freelance data scientist, you might be wondering how to land one. How do you find and connect with employers looking to hire for one of these positions?
You can register as a freelancer on platforms like Upwork and Fiverr. Post actively on LinkedIn.
Learnt about an interesting, new topic? Post about it on LinkedIn.
Created a data science project? Write an article about it. Share your code on GitHub.
If you want to consistently land freelance jobs, your work needs to reach the right people. You need to be vocal about what you do and consistently promote yourself.
Almost every freelance task I got is because an employer stumbled across one of my articles or posts on social media.
Also, keep in mind that many employers choose to hire based on trust. They are more likely to hire you if you are highly recommended by someone they are close to, as opposed to taking the risk of hiring an applicant who uploaded their resume on a job portal.
Once you land your first few freelance jobs, it will be a lot easier to get more of them. The more jobs you successfully complete, the higher your profile will rank on platforms like Upwork.
After completing a task for an employer, ask them for a recommendation. This will provide future employers with confidence that you can get the job done, and increases your chances of getting hired.
Finally, make sure to set reasonable rates based on your level of expertise. In the beginning, it makes sense to charge slightly lower to build up your portfolio. As you start gaining more expertise and more jobs start coming in, you can gradually increase your rates.
However, make sure to do proper research so you know what the market rates are. I wasn’t aware of this when I first started freelancing, and did a lot of work for a fraction of the market price.
I once charged a company around $35 to write a 2,500 word article. After a month of working with them, I asked if this could be increased to $70. They negotiated and lowballed, and settled on $60.
I wrote one more article for them, then left. The remuneration simply wasn’t worth the effort I was putting in, and I decided to direct my energy towards tasks that added value.
I’ve turned down multiple jobs after that if they didn’t align with the amount I quoted the employer.
It is important to know your worth. You have spent a lot of time and resources to acquire the skills you have now. You are constantly working to upskill and gain more knowledge, and online courses/learning materials aren’t cheap.
This time and effort should be rewarded fairly, and you need to set your freelance rates accordingly.
If you are looking to become a freelance data scientist, I hope that the advice provided in this article was useful.
Remember, it isn’t wise to leave your full-time job and start freelancing immediately. I suggest creating a portfolio first and taking up freelance roles on a part-time basis. Once you constantly have a steady stream of jobs coming in and establish a certain level of stability, then you can make the transition towards becoming a full-time freelancer.