I Spent $716.46 Talking to Data Scientists on Upwork—Here’s what I learned.

Key Takeaways

a few months ago I interviewed 10 top data science Freelancers on upwork and made a video summarizing my key learnings while this might sound like a very expensive way for me to learn I found it to be an unreasonably effective way to accelerate my entrepreneurial Journey it allowed me to fast forward Time by picking up years of hard- learn experiences through just a few hours of conversation this whole experience is summarized well by a quote from Benjamin Franklin who said for the best return on your investment pour your purse into your head to make this a bit more concrete I want to shed more light on some of the upsides that I've realized since the first round of interviews data science skills are highly valued it's not uncommon for experienced Freelancers to charge anywhere from $75 to $150 per hour and even more specialized Freelancers may even charge anywhere from $200 to $300 an hour this is why any tactical tip that can make you a more effective data science freelancer can quickly translate to a tremendous amount of value however all the lessons and knowledge that I gained from these interviews was not the only upside here are four other benefits that were a bit unexpected the first is that I've maintained relationships with many of the Freelancers that I spoke to which has been an enormous resource and support for me as someone who relies on data Consulting as their main source of income second many of the Freelancers I talked to ended up joining a community that I run called the data entrepreneurs and have shared their expertise through community events and workshops third one of the Freelancers I spoke to actually connected me with a Consulting opportunity which generated about $900 in revenue and then fourth and finally the blog that I wrote summarizing my key learnings from the first round of interviews has generated ated $472 in earnings as of making this video so even these lessons and connections and relationships put to the side my first round of conversations generated about $1,400 in Revenue which is more than double what I spend on the calls so that's why it was a super easy decision to get back on upwork and have a second round of interviews with top data science Freelancers however a key difference in the second round of interviews is I went for quality over quantity which basically means I spent more money to talk to less people more specifically I spent about $700 talking to four top Freelancers while many of the takeaways I talked about in my first video were reinforced in the second round of conversation new points were raised and nuances of past takeaways were revealed in this video I'll summarize these key points and nuances and if you find Value in this content please consider subscribing to the channel that's a a great no cost way to support me on this entrepreneurial journey and all the content that I generate from it so one of the key questions I asked in the second round of interviews was what's the number one reason Freelancers fail I find this question helpful because often success isn't just about doing things right but not doing things wrong this brought up a wide range of responses from the Freelancers which I'll summarize through three key points the first point is misalignment one of the the biggest challenges in data freelancing is a poorly defined business problem or project scope this often leads to miscommunications and a lot of times project failures this seem to especially be a risk when working with non-technical clients in other words working with clients that have little to know experience of data science and AI the most common strategy for navigating opportunities with poorly defined business problems is simply p passing on the opportunity while this is definitely a judgment call that depends on the details of the opportunity this does seem to be a common red flag that successful data Freelancers tend to avoid the second point is that Freelancers fail because they commit too early in other words those who are new to freelancing may feel compelled to give a number too early which means they might commit to something before they fully understand what the desired outcome is or before they fully understand what it'll take to get there one freelancer recommended the following line when clients press for a commitment prematurely they would simply say I do not commit to something I cannot do and the third reason that new data Freelancers will fail is because of unrealistic expectations while freelancing in data science comes with Incredible freedom and income opportunities it is not easy especially early on those who are new to freelancing and expect too much too quickly set themselves up for failure in other words they come in with super high expectations which sets them up for disappointment and giving up too early this reinforced a key Insight from the first round of interviews which was new Freelancers should focus more on repetition and reviews than money one of the key takeaways from the first round of interviews was to find a niche niching is powerful because it gives a freelancer services greater Clarity for prospective clients and it allows them to charge premium prices for their specialized expertise however in this second round of interviews some nuances of niching were brought up one freelancer advised the following don't pige and hole yourself into a single Tech stack or solution the more adaptable you are the more valuable you become in a freelance capacity another freelancer shared a similar sentiment and said a diversified Consulting business is more robust what this all boils down to is that niching comes with an inherent risk it works great as long as there's demand for that specific service or expertise however if that specialization becomes irrelevant niching can be catastrophic just ask any former Blockbuster executive while you might be confused now and ask wait sha do I Niche do I not Niche what am I supposed to do here's my takeaway from these conversations Niche to differentiate yourself but don't lose sight of other opport unities to expand your Consulting business another key takeaway from the previous video was to form alliances across the whole Tech stack the central reason for this is that data science skills can be limited in their business impact and value in other words it doesn't matter how good your r squar or Au is if you can't deploy Your solution into the real world this is why Freelancers from both round one and round two advise me not to only form alliances across the Tex stack but to learn it for myself and I didn't fully appreciate this point until this second round of interviews and heard it reinforced over and over again part of the reason I didn't fully see this is that learning the full Tex St is a tall order these days the Tex St involves data engineering data analysis data science ml engineering and Beyond which are all their own specializations and I definitely had a bit of apprehension and disbelief that one person could be an expert in everything however one freelancer shared good perspective which changed how I looked at the situation they said you don't need to learn everything you just need to learn enough to containerize your script in other words you don't need to be an expert in everything you just need to know enough to get the job done to make things a bit more concrete that same freelancer shared their specific text act with me which is as follows for most things they used AWS and for architecting the data backend they used RDS which is a way to implement a postgrad database and and S3 buckets for building a data pipeline they use tools like ECS ECR kubernetes airite Docker and R modules for building out computational infrastructure they use terraform for writing data science and data analytics code they would use R and for making web apps they used something called R shiny and while I'm not an R developer seeing this concrete example of a full Tex stack was super helpful to me and made this idea of being a full stack data scientist much more accessible the question that I've asked every single freelancer that I've interviewed is where is this going for those who were interested in scaling up their Consulting business two general paths seem to emerge which mirrored the two general career paths that I saw doing data science at a large Enterprise path one is the manager or leadership route this involves less technical work and more people work on the other side we have path 2 which Which is less people work and more technical work both of these paths can be super rewarding and typically come with increasing compensation what I realize through these conversations is that there are two very similar paths in data freelancing and Entrepreneurship so the freelancer version of path one was embodied by one of the people I interviewed their ultimate goal was to continue scaling their Consulting business into an agency where many consultants served many clients while some level of of technical expertise is required to be successful at this scaling this way relies much more on one's business Acumen managerial experience and communication skills conversely we have the freelancer version of path 2 which was embodied by another person I spoke with they had actually tried path one and realized it wasn't for them their preference was to continue doing the technical work and not having to worry about employees subcontractors and all the fixed costs that are associated with scaling a business business like that while scaling path 1 might seem obvious more clients means more money the way path 2 scales here is one simply increases their hourly rate and returning back to the idea of niching in freelance an interesting observation was the freelancer on path one was much more aligned with not committing to a niche and going after client demands while the freelancer on path 2 had found a strong Niche to operate in which allowed them to charge an hourly rate of $200 an hour however for many Freelancers including myself the long-term goal isn't to scale the Consulting business but rather build a product focused business this was the sentiment shared by the two other Freelancers I spoke to while I've received mixed advice from successful Founders on whether freelancing is an optimal path to product development it does check two important boxes for those trying to launch a product the first is flexibility freelancing allows one to turn up or turn down their workload load to accommodate for product development time the second box is that it generates immediate cash flow in other words freelance is a straightforward way entrepreneurs can translate their skills Into Cash with that being said one freelancer and former founder did warn me that freelancing can easily become a treadmill meaning that one can get so caught up in the cycle of Consulting of marketing closing contracts executing Services Etc that they don't end up building that product and long-term Equity this is why they recommended that I reserve time to take a step back and think strategically about how I spend my time and attention to wrap things up here I want to highlight three key takeaways to add to those from my previous video the first takeaway is that Clarity of scope is the most important thing when assessing a freelance opportunity don't commit to any opportunity until you have Clarity this will help avoid many of the challenges that arise in freelance work and help ensure that that the project provides value to both sides the second key takeaway is to never stop learning this goes for both the technical skills and the non-technical skills data science freelancing is unique because it involves ever evolving technology and perishable skills such as communication and negotiation which makes continual learning a requirement to be successful in this field and the third and final takeaway is to find a niche but always have back doors don't Niche yourself out of a job and don't try to be everything to everyone find that right balance that matches your goals and what the market needs if you got value from this content please consider subscribing to the channel that's a great no cost way of supporting me and the content that I generate if you have any questions or insights of your own as a data freelancer drop those in the comment section below and as always thank you so much for your time and thanks for watching