Photo by Alexander Mils on Unsplash

A conventional 9–5 job isn’t the only way to earn an income. Data skills are highly in demand right now, and if you are someone who is able to derive value from large amounts of data, you can take up many high paying tasks outside a traditional full-time job.

I’ve been freelancing for over a year now in this field, and have had the opportunity to work in different domains with people from all over the world.

The best part about taking up freelance data science jobs is the ability to work from home at my own pace. I get to handpick the tasks I’d enjoy doing, and reject jobs I find uninteresting. I also find that my communication skills have improved dramatically as I’ve had to converse and translate findings to people who come from diverse backgrounds.

In this article, I will list 5 different ways you can use your data skills to generate side income. I will also provide you with insight on how you can stand out from other freelancers and consistently land high-paying gigs.
 
 

Tasks to Generate Side Income

1. Building Machine Learning Models

Many small organizations and startups don’t have a data science team in place. These companies prefer to save cost by outsourcing their data science work to an external hire on a contract basis.

This means that not only will you be working with these companies to build models on a one-off basis, but you’d also be required to continuously monitor performance and update the algorithm with new data when required.
An engagement like this gives you an advantage since the company will require your services over a longer period of time.

2. Data Collection

Many organizations rely on third-party data for competitor intelligence, building pricing models, performing sentiment analysis, and staying ahead of other players in the market.

A freelance task like this would require you to use APIs to collect data. I once had to collect Twitter data with the help of an API for a client who required social sentiment data over the past 5 years to generate stock market predictions.

Depending on the complexity of the data collection task, you might also need to crawl the web to extract information from websites. I’ve scraped pricing, review, and product data for a few different companies in the past. In some cases, it sufficed to store this data in an Excel sheet. Other clients wanted me to create and regularly update a database consisting of all the scraped information.

Web scraping is an incredibly useful skill to have, since many organizations don’t have internal data and rely almost entirely on publicly available data sources to gather intel. If you aren’t familiar with scraping and would like to learn it, I suggest starting with this DataCamp course that will take you through data collection with Python and BeautifulSoup.

3. Market Research

Market research is a field that involves sizing up a company’s target audience to gather information about potential customers.

In the past, most market research was done by marketing professionals, who would conduct surveys, interviews, and create customer focus groups.

However, these methods come with their drawbacks. Surveys can provide companies with biased results depending on the nature of the audience they are sent out to. They aren’t always indicative of the true population. Furthermore, potential consumers can always respond to surveys incorrectly due to time constraints, especially if there is a completion reward involved.

Also, some forms of market research require you to work with large amounts of external data collected from online sources. And while marketing experts might be great at interpreting this data, they might not have the skills required to analyze it due to their lack of data literacy.

The factors above have led to a shift in the role of a market researcher. Nowadays, companies are on the lookout for someone who possesses data analysis skills and marketing domain knowledge.

I’ve worked with a few companies on market research tasks in the past. My job usually started with collecting publicly available data, cleaning it, and storing it. Then, I would perform an analysis of this data and attempt to interpret it using some marketing domain knowledge. Finally, I’d come up with overall market insight and relevant recommendations, which I’d hand over to the company’s product or marketing team to take action upon.

If you are a data scientist who would like to expand your skillset and gain some domain knowledge in market research or marketing analytics, here is a course I suggest taking. 

4. Data Science Content

I’ve worked on creating many forms of data science content in the past?—?blog posts, tutorials, whitepapers, and opinion pieces.

There is huge demand in this field for a person who is able to condense highly technical material and make it easily digestible. 

Platforms like Medium, Analytics Vidhya, and KDNuggets are a great way to build your online presence and get paid as a writer. 

I discovered Medium around two years ago and started writing articles on the platform. I find that my storytelling skills have dramatically improved since then, along with my data science and writing technique. 

Also, as I garner more readers on the platform, I’ve received multiple job offers from employers looking to hire freelance data science writers.

If you’d like to learn more about how to get started writing for data science, I strongly suggest reading this guide I created some time ago. 

5. Data Science Instructor

Data science and analytics skills are highly in demand nowadays. And it isn’t just data science aspirants who are trying to learn the subject; even non-technical executives in organizations struggle to work with quantitative data, and are willing to attend training programs in the field.

You don’t need to be an expert at every aspect of data science to become an instructor in the field. For example, if you aren’t well versed with machine learning modelling but have extensive knowledge of SQL, you can just choose to teach a beginner level SQL class for newcomers to the industry.

How to Land High Paying Data Science Gigs

Now that you’re familiar with the types of tasks you can take up as a data scientist, you probably have one pressing question on your mind:

“How in the world do I actually find these jobs?”

I’ve tried multiple times to earn a side income from platforms like Fiverr and Upwork, but these platforms were simply too competitive. Every project I was willing to take up, there were at least five other people willing to bid for the job at a lower rate.

I shifted my focus away from freelance sites, and instead started to showcase my work to a wide range of people on LinkedIn. I also signed up to platforms like Medium, where I wrote articles describing the projects I’d worked on in the past.

As more readers started noticing my work, job requests started to pour in. 

For instance, I once created a customer segmentation model and wrote a tutorial about it on Medium. I didn’t expect many people to read it. The next day, however, an employer reached out to me on LinkedIn asking me if I’d be available to work on a segmentation project for his company. It’s been over a year, and I still work with this person on many different projects on a contract basis.

I believe that building a social presence and sharing your work with other people is vital to get into freelancing, especially in a competitive field like data science. 

Once you’ve worked for multiple clients and built a strong portfolio, ask your customers for positive feedback and high ratings on freelance platforms. This will help you get noticed easily on these sites, and you can land future gigs even without having to bid for jobs.

 
 
Natassha Selvaraj is a self-taught data scientist with a passion for writing. You can connect with her on LinkedIn.