Finding freelance opportunities as a data analyst is worth the time and effort. Sometimes, it takes months to land your first gig, but it soon starts to build up into something bigger and you have a pool of clients you cater to.
Smartest and usually the best paying way is to tell your network you are available for freelance work. Show what you can do and have a portfolio of problems you have solved through data. You should have ideally worked on real business problems that your skills helped solve.
If you don’t have a network, then go on freelancing websites.
Try Upwork(www.upwork.com) for a start. You will not make that much to start and will need to put in a lot of time applying to gigs, but once you have collected a few very positive reviews you can increase your rate. Search for a ‘client’ on the site to understand your competition and get a sense of the specializations that are interesting to you. Then create a very compelling profile that supports your knowledge in the data science domain. A lot of companies and startups are looking for marketing analytics and dashboarding skills. You would also find people looking for talented data analysts to leverage their data and help monetize their user traffic.
Getting your first client is a challenge.
Your first breakthrough is always hard. Look out for specific forums on Telegram, LinkedIn, and Facebook, where you can introduce yourself and the skills you possess. There are many communities for data enthusiasts and recruiters/startup founders are active on these sites.
To get more traction try: Reddit, Upwork, Fiverr, and Freelancer (www.freelancer.com).
Have something you can share.
Sometimes your work might be under NDA and the best thing to tackle this is to work with public data and take part in online Hackathons. A lot of recruiters look for candidate profiles on Kaggle, Leetcode, CodeChef, and Hackerrank. You need to solve as many problems as you can on these platforms and post about your journey on LinkedIn. Having a personal website is wonderful. Speak about your journey on your site and showcase your work!
When a client approaches you, try as hard as you can to link some of your experience to their situation, even if it's remotely connected, it's better than nothing.
To decide how much you should charge, look up on google.
Google [Your Skillset] Upwork and you'll get a list of 10 people who charge that rate. Google your competition and ask them for quotes. Google full-time jobs and adjust accordingly. Tweak it to what you are comfortable billing, but all of those searches should give you an idea of what the market can bear.
A good rule of thumb is to calculate your market salary divided by the number of hours including taxes. So for example, if you earn 50,000 USD per year gross and you and your working hours range between 37.5 to 40 hours, you can expect to charge between 26 - 27 USD per hour., maybe you can round it by 30 USD. Inevitably you would know if your rate is too high for what you are offering or too cheap, with the number of messages you receive.
You can also try out fixed-price projects with good value.
In other words, where the client/employer has overstated the price of the hour or the number of hours. It is extremely important to find a niche market where you can differentiate enough, such as Tableau Developer, Python Web Scraper, Python ETL Engineer, etc.
Finally, make every application relevant to the client.
No one wants to read a copy-paste of your cover letter. Having sample projects or your entire portfolio also helps the client to trust you. There is no issue accessing clients' data, they would give you access to their data sources and sometimes you will need to sign an NDA.
Finding freelance opportunities as a data analyst is worth the time and effort. Sometimes, it takes months to land your first gig, but it soon starts to build up into something bigger and you have a pool of clients you cater to.
Smartest and usually the best paying way is to tell your network you are available for freelance work. Show what you can do and have a portfolio of problems you have solved through data. You should have ideally worked on real business problems that your skills helped solve.
If you don’t have a network, then go on freelancing websites.
Try Upwork(www.upwork.com) for a start. You will not make that much to start and will need to put in a lot of time applying to gigs, but once you have collected a few very positive reviews you can increase your rate. Search for a ‘client’ on the site to understand your competition and get a sense of the specializations that are interesting to you. Then create a very compelling profile that supports your knowledge in the data science domain. A lot of companies and startups are looking for marketing analytics and dashboarding skills. You would also find people looking for talented data analysts to leverage their data and help monetize their user traffic.
Getting your first client is a challenge.
Your first breakthrough is always hard. Look out for specific forums on Telegram, LinkedIn, and Facebook, where you can introduce yourself and the skills you possess. There are many communities for data enthusiasts and recruiters/startup founders are active on these sites.
To get more traction try: Reddit, Upwork, Fiverr, and Freelancer (www.freelancer.com).
Have something you can share.
Sometimes your work might be under NDA and the best thing to tackle this is to work with public data and take part in online Hackathons. A lot of recruiters look for candidate profiles on Kaggle, Leetcode, CodeChef, and Hackerrank. You need to solve as many problems as you can on these platforms and post about your journey on LinkedIn. Having a personal website is wonderful. Speak about your journey on your site and showcase your work!
When a client approaches you, try as hard as you can to link some of your experience to their situation, even if it's remotely connected, it's better than nothing.
To decide how much you should charge, look up on google.
Google [Your Skillset] Upwork and you'll get a list of 10 people who charge that rate. Google your competition and ask them for quotes. Google full-time jobs and adjust accordingly. Tweak it to what you are comfortable billing, but all of those searches should give you an idea of what the market can bear.
A good rule of thumb is to calculate your market salary divided by the number of hours including taxes. So for example, if you earn 50,000 USD per year gross and you and your working hours range between 37.5 to 40 hours, you can expect to charge between 26 - 27 USD per hour., maybe you can round it by 30 USD. Inevitably you would know if your rate is too high for what you are offering or too cheap, with the number of messages you receive.
You can also try out fixed-price projects with good value.
In other words, where the client/employer has overstated the price of the hour or the number of hours. It is extremely important to find a niche market where you can differentiate enough, such as Tableau Developer, Python Web Scraper, Python ETL Engineer, etc.
Finally, make every application relevant to the client.
No one wants to read a copy-paste of your cover letter. Having sample projects or your entire portfolio also helps the client to trust you. There is no issue accessing clients' data, they would give you access to their data sources and sometimes you will need to sign an NDA.