What I Learned in my First 9 Months as a Freelance Data Scientist
Introduction
I can't believe it has already been 9 months since I have been working as a freelance data scientist! I originally wrote about making the leap after I was 3 months in. Getting started my husband and I agreed that we would try it for 3 months and that we would know after 3 months if it was going to work. I am very pleased (and fortunate) to say that we knew after about a month that it looked like freelancing was going to work for us.
My original post garnered a lot of public and private questions from people facing layoffs, return-to-office mandates (which are typically layoffs in disguise), and burn out. I also have learned a lot more in the past 6 months about how to make this work. I also made some key mistakes and have learned from them on some things not to do. So I thought it was time to post an update to the original post.
I do recommend that you read my original post first because there are some important things in there that I will not cover here. Like last time, I would also like to give a shout out to Brett Trainor who has created on online community (it is mostly Gen X-ers, but applicable to most people) called The Corporate Escapees dedicated to helping people free themselves from corporate work. He also has a great presence on TikTok. Brett and the "Escapees" have been great to bounce ideas off of and provide a wealth of information on getting started and flourishing while working as a freelancer, fractional, consultant, or solopreneur.
More things I love about having gone solo
In my original post I had laid out several things I love about having gone out on my own. These things included stuff like working on what I work on when I want to work on it and making my own rules for my company. In the past 6 months I have discovered some new ones and learned more about the ones I already knew. So I thought I would briefly describe them here.
First, I (still) get to work from home. I have been working from home since 2017, which has given me the freedom to live where I want. I am a mountain creature and so the idea of moving to some random city away from all of the outdoor activities I love is not at all appealing to me. (In fact, I left the field of my PhD so that I could take a remote job…something that was not possible in that field.) More importantly, we are seeing many companies that went remote with the pandemic issue return to office (RTO) mandates. It is well documented that many of these are layoffs in disguise. So in reality, I have more job stability as a freelancer! And those companies that are laying off still have work that needs to be done. They just don't have the positions to do it because they have laid people off. This means that freelancing is actually going to be more stable over the long run, because these companies will need to bring in someone to do the work!
Next – and this is no small one – is that I no longer am subjected to the huge waste of time that is a performance review. I have watched coworkers turn themselves inside out writing their self assessments fretting over every single punctuation mark only to have them essentially ignored. I have written many self assessments that included jokes and laughable things that managers have never commented on or noticed because they never read them! The process of performance reviews is broken. And what is the point when the majority of the time "meets expectations" lands you a so-called raise that is less than the increase in the cost of living?
This is not to say that as a freelancer your performance doesn't matter. It is just that you don't have to get all anxious and waste your time listening to your boss rattle back to you some list of accomplishments, tell you that you are doing a good job, and not give you too much reward for it. Instead, the reward for a freelancer (or the management of a poorly-performing one) is done through repeat business. Does a client choose to renew or extend your contract? Are they giving your name out to their friends as a referral? If so, job well done!
One that should not be overlooked is the fact that I have control over how my company-of-one runs. This has a lot of different impacts. First, I do not have to go ask numerous managers permission to work on a thing. If I want to work on something, I work on it. Ideally, there are others who need that type of work done that I can help. Second, I determine the finances of the company and don't need to ask permission to attend a conference, take a class, travel to meet with a prospective client, or buy a new software tool. While it might sound silly, I actually am giddy (in a geeky kind of way) that I get to pick my own computer. I greatly dislike being forced to use Windows or Mac (Pop_OS FTW!!!) or being tied into a particular type of hardware. If I think it would benefit my business by me attending or speaking at a particular conference, I go and don't need to play "Mother May I?" to get permission to go. If I decide I need to buy a particular book for my continuing education, I don't need to ask someone. There is great freedom in this! (And, by the way, these things are tax deductible as well!)
On the importance of networking
Definitely the most common questions I have received since writing my initial post 6 months ago have to do with networking. When you are working as a freelancer, the old saying goes: "your network is your net worth." There are a lot of implications to this statement and not all of them are pretty. So I am going to share some hard truths here.
First, networks are established over time. Good networks include people who tend fall into one of a few categories:
- People you have worked with in the past and are familiar with your work
- Other people in your field who know of your experience, skills, and interests
- People who work for companies that have problems that you can solve
(Note that this is not an exhaustive list, but you get the point.)
When you are freelancer you are selling a brand and that brand is you. Think about it like buying a car. You are not going to buy a car that is a brand you have never heard of. Further, you are not going to buy a brand that has not made a car before just because they have an assortment of parts. People buy things they trust.
What this means is that it is really hard to be a successful freelancer – in Data Science or otherwise – if you have not already been working as a data scientist for some period of time. When clients hire freelancers they are trying to solve a problem. They want to know that the freelancer they are hiring knows how to solve it and has experience in doing so. This means that it is very difficult to be a successful data science freelancer as your first job out of school. Even graduate school. Real-world experience is highly valued by those who look to hire freelancers.
The good news is that the act of getting that so-called real-world experience is already a key step in developing your network (i.e. the group in the first bullet point above). Many freelancers I know have their previous employers as some of their first clients. This is also why it is really important to avoid burning bridges with those you have worked with in the past, because you never know could be your client in the future!
In my previous post I suggested things like conference attendance and speaking as ways to grow your network further. I still hold to that. However, it is also important to recognize that not everyone does well at conferences. There are some neurodivergent people who find conferences to be difficult. And that is OK! There are ways to build your network beyond conferences, especially including things like blogging here and elsewhere! What you are looking to do, whether at conferences or blogging, is to grow your brand and brand awareness. When people think of you as a data scientist, what types of things do you want them to think of? Maybe you are a whiz at forecasting. Blog about it! Maybe you really enjoy writing code to solve a certain type of problem. Create a YouTube video about it!
Increasing your brand awareness (and network) through a good portfolio
The important thing here is about creating that brand awareness of the brand that is you. This, of course, means that people need to be able to find your brand and learn about it. Particularly if your network is not large, this means that people need to be able to see your work. Here is where creating a really awesome portfolio can help. Getting your portfolio in front of people in the last category above can help you grow your network and land jobs.
There is a ton of content out there about how to create a good data science portfolio. I will just summarize some key points here.
First, your portfolio should use an interesting data set. Do not use any data set from educational forums such as the Titanic data set, MNIST, cat versus dog via imagery, etc. Kaggle, while a great learning tool, does not always reflect the real work. You want the data to be as realistic as possible. It should be noisy and messy because this is how real-world data is. It should answer an interesting question (bonus points if it is an interesting question that could make a company money or solve a big problem). And it should also be data on a subject you are interested in and knowledgeable about so you can personally identify if the answers make sense and talk people through it like a subject matter expert.
Second, you need to tell a complete story for each project in your portfolio. Do not just put up a bunch of code with no explanation for how to use it. Do not provide results from some model without an explanation of what is going on with the model and the data and what conclusions should be drawn. A good portfolio project is as much about the explanations of your work as it is about the code. Your explanations should be extensive. You need to demonstrate that you not only know how to code, but know how to walk the reader through the full start-to-finish story of problem to solution.
Your portfolio projects, when possible, should be interactive. You want people to be able to see that the code runs. I personally am a big fan of setting up an inexpensive virtual machine somewhere and running Streamlit dashboards for interactivity.
Because your portfolio is about brand awareness, think about what your brand is. For example, if you are wanting to advertise yourself as being really good with recommendation engines, don't waste time demonstrating solutions in image analysis. You are going to be showing your future clients the types of problems you can solve for them. The more obvious you make that, the better.
Finally, whenever you make an update to your portfolio, you need to get the word out there. Make a blog post or YouTube video to go with it. Make the code publicly available on GitHub or GitLab. Post on LinkedIn links to the portfolio and point out the new content. Post another link once the blog post is published.
Be as much of a generalist as you can
I love being a specialist. Many people do. I have some pretty deep knowledge in some pretty specific domains. However, being a freelancer is about solving a problem. Frequently (and especially with startups that don't have many employees) you will be expected to know how to do more than create the small, superb solution to the problem. You will need to know how to solve the problem from beginning to end. This means that you will need to work beyond that small, niche skill.
For me, this has meant that I have been learning (an ongoing process) many skills that go beyond my favorite areas of graphs, NLP, LLMs, etc. I have had to learn a fair bit more about data engineering, MLOps/LLMOps, and cloud architecture. I am paying to take classes and go to conferences on these subjects (see above…my management approved it
