Are you looking to make a stand-out data science freelancing portfolio? Well, in today’s post, I’ll show you the great, the passable, and the uglyAF in the world of data science freelance portfolios and I am going to teach you how to think for yourself on these matters, and craft a custom portfolio that attracts freelance clients that are perfect for YOU!

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Even if you’re a seasoned data science freelancer, I know these steps are going to be SUPER valuable to you in pin-pointing some of the missing pieces from your freelancing portfolio that are probably holding you back from reeling in some of the higher-end clients that truly need your help!

How do I know? Well, because I first started out as a freelance data scientist waaay back in 2012… After serving 10% of Fortune 100 companies from within my own data business, I started coaching other data professionals on how to hit 6-figures in their own businesses FAST. 

Between my personal experience and what I’ve garnered from those of my client, I’ve become a true master in monetizing data skills.

My name is Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs.

Brace yourself because you’re about to see quite a few astonishing data science freelancing portfolios. But, first let me explain why I am even sharing all this…

 

The Backstory

To do this, I need to introduce you to 2012 Lillian – when I first started data science freelancing. I had a full-time day job, and was working at night to build my brand online. I would get paid requests for help here and there and would, of course, take them. 

Lillian as a data science freelancer in 2012

The thing is, none of these jobs paid well. I would earn $200 to $250 for 5 to 10 hours per work… but I had to do that work on top of my day job and on top of the marketing work I did to build my brand to get those paid job offers anyway… 

Eventually I had enough of these freelance data science clients to quit my day job and move to Thailand…

What I didn’t do is, I didn’t focus on a specific buyer avatar and service. I never said no and just took everything and anything that came my way. That’s when I had 10 different freelance clients from 10 different industries, all underpaying me to do 10 different types of services. 

 

This is literally what I looked like by the end of 2014…My “business” was not scalable, not all that profitable, and definitely not fun. I don’t want that to happen to you, which is why I am giving you the cliff notes on how to get ahead. 

 

 

 

 

the low-budget hamster wheel

 

 

Actually, one of my clients, Kam Lee – even named that phenomenon, the low-budget hamster wheel. Back in 2020, he joined my mentorship program so he could escape that hamster wheel… 

 

 

 

 

revenue of a startup company, Finetooth Analytics

 

 

And wouldn’t you know – in just 1 year – not only did he get off the hamster wheel, he achieved this:

 

 

 

 

 

 

IF THERE IS ONLY ONE THING THAT YOU GET FROM THIS ARTICLE, LET IT BE THIS:

The data science freelancing portfolio that gets you paid – and paid well – is NOTHING like the data science coding portfolio you’d publish if you were looking for a “job” in data science.

How this works is – I am going to show you some examples of Great, Passable, and Terrible data science freelancing portfolios…Then after that, I will sum them up with main takeaways and advice so you can make sure that your portfolio winds up in the “great” category.

The Great

I found this profile in Upwork when I searched for “analyze survey data.” What’s great about this profile is it clearly states the outcome by saying – “You will get an insightful analysis of your survey results.” That’s what she gives you instead of the methods she used in order to produce that outcome. Another nice thing about this profile is, her portfolio is basically her deliverable. She has given proof she can do this by showing the work that she has done in the past to help people evaluate her based on what she has done before. 

great data science portfolio

The next profile that stood out to me is also from Upwork where I searched for “Build AI Software.” I found this great because of his headline which states “Powerful AI solutions for your next AI project or startup” that clearly speaks to the buyer or to the person who is actually making the purchasing decisions

Good Upwork profile

I also liked how his values were written out in plain language which speaks to non-data professionals. He’s obviously talking to business owners who wanted to get the job done and are willing to pay his rates which is the kind of thing you need to do in your portfolio. 

Great Upwork Profile

The third one I found over on Fiverr is not super great but his profile is very specific and niche down. He’s only working on Einstein Analytics so his profile is the only one that will come up if anyone searches for Einstein Analytics. Then, he also started his profile with who he he’s there to help and how he helps them – it’s not about him but it’s all about the service.

Hey, share your thoughts please – tell me in the comments…

What features of these first few portfolios really stood out to you as AWESOME?

Now let’s look at some passable data science portfolios, what they do well, and how they could be improved…

The Passable

This portfolio is just a bunch of thumbnails that only data professionals can understand. And if he’s selling to other data professionals then he’s not gonna get any good rates, it’s going to be a race to the bottom. What I did like about this profile is at least he niche down and he was able to specify his domain i.e., healthcare, banking, etc., so it’s not just “I do data science for everyone.” He also mentioned the business benefits of the service he renders. 

But then this is not a portfolio, this is just a profile that doesn’t show any work that he’s done in the past. He can make this better by showing pictures or a video of the product he created to demonstrate the deliverables that he’s able to create.

Here’s another decent profile I found on Fiverr – it’s also specific to Google Data Studio and it’s going to come up right away if anyone searches for GDS. His dashboards are decent and he’s showing the products he’s done in the past. However, his profile focused too much on what he does, rather than who he helps.

Would it surprise you to hear that 95% of data science freelancing portfolios I saw when I was preparing for this post fell into the ‘terrible’ category? It’s true though. Let me show you what you want to make sure to avoid doing…

The Terrible

If you look through Fiverr, there’s a whole data science section and you’ll see 120 profiles that all basically say “I will do machine learning, data science, python,” etc. and none of them stood out, because they didn’t include what exactly they do, who they help and how they can help them. They also didn’t show their deliverables which is very important in creating your portfolio.

data science section in fiverr

This is a DO NOT ENTER zone: 

If you’re a business owner looking to hire someone, you’re not gonna understand these bunch of codes or graphs where you can’t read anything. It basically shows that the guy knows how to use functions in python and that is it. We don’t even know if he created that. 

terrible data science freelancing portfolio

A business owner who is actually looking to hire is not gonna know what this mess is – furthermore, is not going to care. They care about getting the business objectives met and this kind of graph isn’t gonna do that. Whatever you provide them from your data science services needs to be written out for them to read and it should clearly states the deliverables and how they can use it to generate value for their business.

Bad portfolio

 

If you’re vibing with all this data science freelancing talk, you’ll probably love the video I did on “How to Become a Freelance Data Scientist.” Check it out here.

The Recap

Now, let’s talk about what you need to do in order to have your data science freelancing portfolio really serve you as effectively as possible. 

  • Showcase the deliverable
    • If you have past work then you can showcase the deliverables on those products so long as it doesn’t violates any contractual agreements you have in place.
    • If you don’t have any past work, then you can of course create your product. A product can be anything like a dashboard, a report, an application or SaaS, if you have one. You can also create something like a roadmap, a course, a book or any sort of deliverable that demonstrates your skills and competencies with respect to delivering the service. 
  • Be specific – start with exactly who you help and how your service benefits them.
  • Stay focused on the ROI – Just 1 or 2 sentences that mention vague references to business or industry value is not enough. Every single aspect of your portfolio and freelancing profile needs to be hammered down hard into how your service actually either creates new revenue for your clients or save them money.

 

The Secret Sauce

I showed you a lot of cool examples here and some of them are pretty reproducible, but if you really want to be successful as a data science freelancer, I recommend you not to copy the accounts I just showed you.

Why? Because your background is different from those people. Your talents, skills, passions and education are all different. While you could possibly do something like analyze and visualize survey data – that’s probably not what’s most interesting to you AND it’s probably not what the world needs the most right now. Don’t be the person who shows up late to someone else’s game, be the person who innovates their own game. That’s going to be the most fun to you. That’s the area that’s most untapped. 

Here are the things that you need to do to create a great portfolio that will surely stand out:

  • Get creative, and stay authentic
  • List out all your data skills
  • Circle the ones you love the most
  • Think about freelance clients and what their needs are
  • Match your skills against a buyer avatar that you really like
  • Build a freelance portfolio and freelancing account specifically for that buyer avatar
  • Seed your account with the keywords and search terms that the buyer would use if he was searching for help in his business problem
  • Place yourself in a position to be found by that person
  • Wow him with your deliverables and products inside of your data science freelancing portfolio
  • Be sure to emphasize and demonstrate how your data science services will be useful for that buyer avatar in creating a massive uptick in his business’ bottom line.

 

And that, my friend, is the secret sauce to being a showstopper in the data science freelancing industry. 

 

If you’re digging this not so brief guide to building a badass data science freelancing portfolio, then I’m sure you’re going to love my FREE Data Entrepreneur’s Toolkit.

It’s an ensemble of all of the very best, most efficient tools in the market I’ve discovered after 9 years of research and development. A side note on this, many of them are free, or at least free to get started, and they have such powerful results in terms of growing your business. These are actually the tools we use in my own business to hit the multiple 6-figure annual revenue mark.

Download the Toolkit for $0 here.

You may also love it inside our Data Leader and Entrepreneur Community on Facebook. It’s chalked full of some of the internet’s most up-and-coming data leaders and entrepreneurs who’ve come together to inspire and uplift one another. 

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Lillian Pierson, P.E.

Lillian Pierson is a CEO & data leader that supports data professionals to evolve into world-class leaders & entrepreneurs. To date, she’s helped educate over 1.3 million data professionals on AI and data science. Lillian has authored 6 data books with Wiley & Sons Publishers as well as 8 data courses with LinkedIn Learning. She’s supported a wide variety of organizations across the globe, from the United Nations and National Geographic, to Ericsson and Saudi Aramco, and everything in between. She is a licensed Professional Engineer, in good standing. She’s been a technical consultant since 2007 and a data business mentor since 2018. She occasionally volunteers her expertise in global summits and forums on data privacy and ethics.

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