5 Reasons Why Every Data Scientist Should Consider Freelancing

Key Takeaways

are you feeling stuck you got the degree you got the job you're working in data science you're killing it but for some reason you don't feel like you're progressing in life you're not growing you're not developing the skills that you want to develop or maybe you haven't even gotten the job yet you're desperately applying the jobs you're getting scared all the tech companies are laying people off what am I supposed to do about this how am I supposed to get a data science job with just a bachelor's degree with my Master's Degree with my PhD even if you resonated with any of these random points I just spat out at you then this video is for you hey everyone I'm Shaw and as someone who has worked in data science as both a freelancer and a full-timer I just wanted to share a little bit of my experience for those pondering and reflecting on their data science journey and trying to figure out where they want to go with it toward that end in this video I'm going to break down five reasons why every data scientist should at least consider freelancing so for my view these points are beneficial to anyone in data science but perhaps especially so for those just getting started in my personal experience freelancing accelerated my development as a data scientist and it played a big part in helping me get my current full-time role as a data scientist and even if your goal isn't to get a full-time gig at a company freelancing can serve as a main source of income or even give you insights into different industries that might help you develop a new business or a minimum viable product so if you like this content and you want to see more about data science productivity entrepreneurship go ahead and hit that subscribe button right now so the YouTube algorithm will know that you want to see this face on your computer screen and with that let's get into the Five Points okay so the first reason why every data scientist should at least consider freelancing is to work on new problems one of the greatest powers of data science that I personally just love so much is that data science is so often context agnostic so what do I mean by that basic basically all I mean is you can take one method one Technique One Piece of code and apply it to multiple different use cases so like a very simple one is logistic regression you can use logistic regression to solve binary classification problems which comes up in credit risk modeling will the person I'm giving a loan to pay me back or analyzing customer retention what's the probability that our customer will continue our services next month or even marketing analytics what's the probab ability that a user will buy our product after they watch our ad and so these are completely different context we're talking about credit risk and financial services lifetime customer value with retention analysis and then we're talking about ads and marketing with that last Point completely different context but you can use a single data science approach to solve all of these problems and these are just three off the top of my head there are countless use cases and applications for logistic regression or any common data science approach and so all that to say when it comes to freelancing you have the opportunity to use this basic toolkit in a wide range of contexts so when I was freelancing I was working as a graduate research assistant in the physics department but through my freelance work I gained exposure to different fields so one example was trying to classify sepsis subphenotypes so basically subtypes of sepsis using unsupervised machine learning techniques that I've used in count other contexts so that's one thing to consider when thinking about freelancing and data science you have the opportunity to work on different problems leveraging your experience and the skills that you've acquired in new contexts and it kind of enriches your understanding of those tools so every time you use a technique in a different context it helps you build an intuition of what else you can use that same method for so reason number two is developing soft skills so when you're freelancing you're really on your own you have to figure out how to put yourself out there how to get clients and how to communicate with a diverse set of people and so when you're freelancing you're trying to find gigs you often have to network you have to talk to people you have to connect with people and this really forces you to develop your soft skills you know sending out cold messages email etiquette talking to people at networking events reaching out to people on LinkedIn responding to potential clients reaching out to you because they see some work that you've done or they came across your profile while on some freelancing website like upwork or Fiverr and so all these interactions all these reps really allow you to develop these soft skills that you may just not have as much opportunity to develop in a full-time role where the work is more delegated to you and is more stable and you're typically interacting with the same handful of people constantly as opposed to in a freelance role you're constantly interacting with new people and brushing up on those skills so the third reason is fine-tuning your pitch and this has some overlap with the second reason it comes down to your ability to communicate and connect with people but fine-tuning your pitch is really about selling yourself and what I mean by this is fine-tuning your resumés your cover letter or proposals and your interview skills so this is another area where full-time roles and freelance gigs have a big difference and It ultimately just comes down to timeliness so for the full-time role you know you submit your resume and cover letter you spend all this time on it but it's not uncommon to not hear back from that application for weeks or even months sometimes so it's really hard to kind of gauge how effective your resume and cover letter were at conveying your skill set and experience but on the flip side in freelancing the time scale is just much faster if you apply to a gig let's say on a site like upwork the feedback is typically much quicker if someone wants to work with you you will a lot of times hear from them within a few days and if you don't hear back from them in a few days that probably means they moving forward with other candidates or the job's no longer relevant or something like that and so ultimately what this means is in freelancing as opposed to full-time roles you really can get a lot of reps in on your resume and cover letter and get much faster feedback and so what this allows you to do is fine-tune your resume and cover letter to convey your skills and experience more effectively and this is something I definitely benefited from so I was freelancing in grad school so constantly fine-tuning my resume and my cover letter and then eventually when I graduated and decided to apply for a full-time role my resume and cover letter went in a pretty good spot and I could just leverage what I'd learned from freelancing to apply to the full-time gig so I would say to anyone trying to break into data science you know you just graduated or you're about to graduate I would recommend freelancing even if you don't get any gigs and don't get any work through it at least you get these reps you get to fine-tune your resume and your cover letter and hopefully your interview skills through chatting with potential clients and you can leverage this experience and these reps and the feedback for applying to a full-time gig but overall the skill set of selling yourself being able to communicate your skill set and how your experience and skills are relevant to solving other people's problems is a very valuable skill set to have and essentially being able to sell yourself and your ideas is something that will be valuable in whatever context you find yourself in reason number four is flexibility and autonomy and so so this is one of the greatest benefits of freelancing and I feel one of the main reasons why people are so attracted to it in freelancing you essentially choose what you work on because you choose the clients that you work with and moreover freelancing gigs are typically on a much shorter time scale than full-time roles so you could be working with a client on a month-by-month basis and it could be going great for 6 months but then at a certain point the work May no longer be relevant or getting overloaded on contracts with a handful of other clients and then you have the option and opportunity to reduce your workload or refocus your efforts toward a specific type of work and also you don't just get to choose what you work on but you typically get to choose where you work how you work when you work and this is something that a lot of people have value in people who greatly value their autonomy their flexibility their freedom you know maybe they don't want to be bound to a certain city they want to be able to travel and you know this was something that got big during Co you know people were getting gigs online or during remote work they were living for months in different countries you know living in Europe or South America or Asia or something like that and so for people that that lifestyle is appealing to them freelancing is a great option for that okay so the fifth reason is networking and so I kind of touched on this before but here what I'm specifically talking about is building new relationships and new connections and so through my freelancing I've met a wide range of people that has given me insight into Worlds that I didn't even know existed so I've worked with medical doctors clinicians with people working in Special Forces military police officers business people you know so many different walks of life and backgrounds and has really enriched my own experience and my understanding of the world which I find a lot of value in relationships and learning from people is something I give a lot of weight to so this is one aspect of freelancing that I really enjoy okay and if those five reasons were not enough to make you consider freelancing in data science I've got two bonus tips to share so the first bonus tip is money so even if you don't really care about developing your technical skills expanding your experience and Horizons developing your soft skills building new relationships what else did I talk about fine-tuning your pitches your ability to sell yourself all these different reasons you could always just do it for the money and most of the time freelancing gigs are much more lucrative than full-time roles so just speaking from my personal experience before I entered into my current full-time role I had two offers on the table I had the my current role and I had a essentially a contractor role which could have been full-time and just comparing the pay of the two roles the contract role paid almost twice as much as my full-time gig I would say 80% yeah paid about 80% more than my full-time gig which is a lot of money I'm just saying that to give you an idea of how much more you could get as a freelancer as opposed to a full-time role and to those who are saying like oh freelancing is so great why didn't you take that why didn't you take the money and that was just a personal decision for me when I had graduated I had done the freelance stuff I'd worked in research but I never worked at a large company as part of like a big data science team the biggest team I had worked with was my research team which was about 12 people and I work on a data science and analytics team that's I want to say like 100 people if not more and so for me the reason I went with the full-time role is because it was a new experience for me it was also the opportunity to learn from other data scientists and data analysts that have been working in the field much longer than I have and then the last thing I'll say about the money is that you know the great thing about freelance is that you can customize the freelance workload so you can definitely be a full-time freelancer and reall the benefits there but if you're just trying to make some extra cash on the side and you have a full-time role you can probably just pick up one or so contracts every so often if you just want to make some extra cash on the side and the second bonus tip is that freelancing gives you options if you have freelancing experience or you've done it in the past you always have that option on the table so say you're working full-time and you want to make some extra cash you can always just go to freelancing or kind of given all the recent Tech layoffs it's kind of a scary thing you could just wake up one day and your full-time employer says we don't need you anymore or we don't see the value in data science anymore and they lay you off now what are you going to do well if you're freelancing on the side or a freelance in the past you have an immediate thing you can fall back on until you can either work up your client base or find another full-time role okay so that's basically it so in this video I give you five reasons why every data scientists should at least consider freelancing with two additional bonus tips and so if you enjoyed this content you want to read more take a look at the blog associated with this video published in towards data science on medium if you enjoyed this content please consider liking subscribing and sharing your thoughts in the comments section below and as always thank you for your time and thanks for watching