The job landscape today has changed dramatically due to the Covid-19 pandemic. Alongside their full time jobs, people now have the flexibility to take on side hustles that serve as an additional source of income.
When I transitioned into the data science field, my initial goal was to land a job in the industry. Once I got a day job, however, I realized that I had the capacity to do more. Working from home meant I didn’t have to socialize as often. I no longer had to travel back and forth to work.
This didn’t just save time. It saved energy. I no longer exhausted at the end of a work day, which meant that I was able to take on tasks outside of my job.
In this article, I will walk you through my experience freelancing for data science. I will also provide you with tips on how you can get started as a data science freelancer.
As a freelance data scientist, you can build machine-learning models for organizations on a one-off basis. Sometimes, you might even get paid to continuously maintain and update this model as new data comes in.
However, your options aren’t limited to model building.
Since my full-time job is in the marketing domain, I have some experience in this area. I use this, along with my data skills to help clients identify their target audience and come up with marketing strategies.
Another highly in-demand skill is data collection. I’ve worked with individuals and companies to scrape external data to help with their research or model building tasks.
I’ve worked as a freelance technical writer for quite some time. I write data science tutorials and tips for publications — either one-off or on a contract basis. I’ve also been asked to conduct data science training workshops and online courses for beginners in the industry.
There are many other tasks you can take up depending on your skillset. You can help organizations deploy and monitor their machine learning models. You can consult companies and provide them with recommendations based on data you analyze. If you are an expert at data visualizations, you can create interactive dashboards for clients based on available data.
How to Become a Freelance Data Scientist in 2022
Tips on building a portfolio, getting new clients, and creating a steady stream of side income
Natassha Selvaraj
Natassha Selvaraj
5 days ago·5 min read
Photo by Towfiqu barbhuiya on Unsplash
The job landscape today has changed dramatically due to the Covid-19 pandemic. Alongside their full time jobs, people now have the flexibility to take on side hustles that serve as an additional source of income.
When I transitioned into the data science field, my initial goal was to land a job in the industry. Once I got a day job, however, I realized that I had the capacity to do more. Working from home meant I didn’t have to socialize as often. I no longer had to travel back and forth to work.
This didn’t just save time. It saved energy. I no longer exhausted at the end of a work day, which meant that I was able to take on tasks outside of my job.
In this article, I will walk you through my experience freelancing for data science. I will also provide you with tips on how you can get started as a data science freelancer.
First, let’s go through the pros and cons of freelancing for data science.
Advantages
The best part about having a freelance career is that you get to work with people from all over the world. The opportunities are endless, and you learn to look at a problem from many different perspectives.
You also get to pick the kinds of projects to work on — something that isn’t always possible when you have a full-time job.
Also, as a full-time employee, you only get to work in a single industry. When freelancing, each project you work on will provide you with domain experience in a new area.
When you work on a variety of tasks in many different domains, your portfolio grows. You aren’t stuck with a single way of doing things, and can adapt quickly to new workflows. Your capacity to learn will improve.
Disadvantages
There are few downsides to becoming a data science freelancer. Firstly, there are a limited number of freelance data science jobs available.
It is usually mid to large sized companies that hire data scientists, and these companies tend to hire full-time employees rather than freelancers. There is a higher demand for freelance web developers/designers as compared to data scientists.
A freelancing career also doesn’t ensure job security, and you need to actively be on the lookout for new tasks. Due to this, it is a good idea to keep your full-time job while taking on freelance roles, especially when starting out.
Types of gigs available
As a freelance data scientist, you can build machine-learning models for organizations on a one-off basis. Sometimes, you might even get paid to continuously maintain and update this model as new data comes in.
However, your options aren’t limited to model building.
Since my full-time job is in the marketing domain, I have some experience in this area. I use this, along with my data skills to help clients identify their target audience and come up with marketing strategies.
Another highly in-demand skill is data collection. I’ve worked with individuals and companies to scrape external data to help with their research or model building tasks.
I’ve worked as a freelance technical writer for quite some time. I write data science tutorials and tips for publications — either one-off or on a contract basis. I’ve also been asked to conduct data science training workshops and online courses for beginners in the industry.
There are many other tasks you can take up depending on your skillset. You can help organizations deploy and monitor their machine learning models. You can consult companies and provide them with recommendations based on data you analyze. If you are an expert at data visualizations, you can create interactive dashboards for clients based on available data.
How to find a freelance gig
When people first think of becoming freelancers, their minds jump to platforms like Fiverr and Upwork.
However, simply registering yourself onto these sites and sending out job proposals isn’t enough to get you many gigs. These platforms are highly saturated. In order to get noticed on them, you need to build up your portfolio by landing a few jobs first.
I suggest creating a portfolio outside of these platforms first. Most employers have reached out to me off-platform — through LinkedIn or via email.
I landed most of my freelance jobs through my blog. I’ve been approached by publications to become a technical writer after writing consistently on platforms like Medium.
I once created a clustering model and created a tutorial on Medium, after which I was hired by an individual who wanted a similar model built for his organization.
One client saw my tutorial on scraping Twitter and hired me to build a web-scraper for him. Another company hired me because of an analysis I published on Medium, as they wanted something similar done to identify their target audience.
I’ve also landed a few jobs because of my network. University friends and co-workers have recommended me for jobs in the past.
People prefer to hire when there is trust. You are more likely to get a job from someone who has been assured by their peer of your capability, than a person you just sent a proposal to on Upwork.
If you become a full-time freelancer, one downside is that you end up losing out on this network. You no longer have co-workers or managers to recommend you for roles. Due to this, I suggest joining local data science events or online communities where you get to engage with other experts within the same industry.
