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Gold BlogHow to Succeed in Becoming a Freelance Data Scientist


With recent growth in data science, now is the best time to get into freelancing. The following steps will help you get started with finding clients or help you improve your current strategy.



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Data science is an ever-expanding field. More industries continue to rely on technology for gathering and acting upon vital information, and data scientists are in high demand. However, finding a job that fits your needs can sometimes be a challenge. In these instances, you can turn to freelance work.

Freelance is quickly becoming a popular option for professionals of all kinds. In fact, LinkedIn is rolling out a new feature specifically for helping freelancers find work. With this growth, now is the best time to get into data science freelancing. The following steps will help you get started with finding clients or help you improve your current strategy.

 

1. Build a Presence

 
The first step of the process is to build and curate a strong presence online. You’ll need a website, job listing and LinkedIn page.

Use your website to show off your skills. Market yourself with your best foot forward. Put a portfolio of relevant work experience you have, and update it as you successfully help more clients. Remember, a site must be easy to navigate or else people will click away. Make it easy to contact you and get the information they need.

On Google, you can open a Google My Business listing and connect your website and details. This way, a quick search for data science freelancers will bring up your business. Consider what hours you want to remain open and how potential clients should contact you.

Then, update your LinkedIn and use the new Marketplaces feature to advertise yourself as a freelancer. This last act will help you cover all the bases for drawing in businesses or individuals looking for data scientists.

 

2. Develop New Skills

 
Data science changes with the times. New skills come into play, and you must keep up with the demands of the job. Fortunately, data science is a broad profession — you can apply skills you learned for cybersecurity to machine learning algorithms.

You can also turn to online courses to gain skills you don’t already have. Lynda has plenty of options that will help you develop new skills like data management in blockchain technology. You can add the certificates to your website, boosting your appeal and qualifications for any data science-related job.

As a freelance data scientist, you should know the basics of the job, statistics, programming, data visualization, machine and deep learning, and software engineering. You’ll also need a good understanding of big data. These skills give you a well-rounded approach to all freelance work.

 

3. Work Across Industries

 
The abundant need for data science professionals is another benefit of this job. You’ll find that tech jobs are a necessity across industries, no matter their focus. For instance, travel and banking require data scientists to protect and monitor sensitive information.

In the banking sector, taking a data-based approach provides necessary transparency for clients and the institutions themselves. With new fintechs offering more financial agency options for customers — like crypto banking — banks need to ensure all this control has proper data protection. This allows the industry to move forward with more trust.

This idea applies across all sectors because every institution needs to gather and protect data. The key is keeping your options open. Look into every opportunity and remember that you can work with established businesses, startups and individual customers.

 

4. Use Online Resources

 
While you can use the internet to get certifications, you can take it a step further. Other resources help you network and create the connections you need to take off as a freelancer. Here’s where you should start:

  • Upwork and Toptal are two freelancing-specific platforms that will help connect you to your clients.
  • Gitter is a Slack community where you can interact with other developers, helping each other out on your projects and goals.
  • Kaggle is an all-in-one platform for learning new skills and finding a community of similar professionals to build a client base.
  • Data Science Stack Exchange is a database of forums where data scientists can ask questions and refine their expertise.
  • AngelList and FounderDating are good job-seeking platforms where you can find companies to work for and with. You can use the filters to narrow down your search for data science-specific opportunities.

Start with these four resources. You’ll connect with others instantly and get to know the field a bit better.

 

5. Iron Out the Details

 
Once you make a job connection, you’ll need to work out some of the final details. Consider your hours, contract obligations and pay rate as you partner with a company or individual.

While the hours and tasks will be up to you to decide based on your needs, working out your salary can be a bit more complicated. Check with other freelancers to see what they charge, and alter those numbers based on the project and your skills and experience.

According to Glassdoor, the average data scientist makes roughly $113,000 annually. If you divide that down to an hourly rate, it comes out to a little over $54 per hour. However, if you have skills like mastery of Scala and Spark, you can increase your fee. It depends on your background. Remember, don’t sell yourself short — know your worth.

 

Successful Data Science Freelancing

 
With these five steps, you can begin your career as a successful freelance data scientist. Everyone’s path will look different, but you should start by building yourself up. Then, with a strong foundation, you can accept any opportunity that comes your way.

 
Bio: Devin Partida is a big data and technology writer, as well as the Editor-in-Chief of ReHack.com.

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