Picture by Writer | Ideogram
# Introduction
After I first began my information science profession in 2020, the sector was booming. All over the place you regarded, firms had been hiring information professionals. At the moment, I constructed an information science portfolio and managed to land a number of high-paying purchasers.
I’d write information science content material, akin to white papers, articles, and technical documentation — which paid between USD $500 and $1,000 for 2 days of labor. I constructed easy machine studying fashions and performed analyses utilizing instruments like Tableau and Energy BI. As purchasers began recommending my work and leaving optimistic evaluations, I landed extra initiatives. I labored 5 to six hours every day from my sofa and was fully distant.
Not too long ago, nevertheless, I’ve modified issues up.
I’ve give up just a few freelance jobs for a full-time information science place — one the place I am going to the workplace day by day and work double the hours. And no, it isn’t as a result of the job pays extra. In truth, I made extra money as a contract information scientist than I do now.
So why did I swap from a snug, high-paying freelance job to a full-time place that pays much less?
Learn on and you will find out the three high considerations that led me to taking this motion.
# 1. Constructing Technical Expertise
After I labored for myself, I spotted I would hit a plateau in studying technical abilities. I used to be working extra like a machine, producing repetitive outcomes for a similar freelance purchasers. This meant that I not solely labored much less, however my technical data had reached a standstill.
A actuality examine got here after I attended a good tech convention and networked with different information professionals. I spotted I hadn’t stored up with a lot of the expertise they mentioned. These information professionals had been constructing AI brokers and retrieval-augmented technology (RAG) techniques, whereas I used to be refreshing the identical dashboard for the hundredth time and writing white papers on Python for information science.
Do not get me incorrect — an information scientist’s worth is within the outcomes they drive, and in lots of circumstances, fancy instruments like massive language fashions (LLMs) are akin to utilizing a sledgehammer to crack a nut. Nevertheless, I lacked primary data of instruments that had been on the forefront of tech firms, and that scared me. I’ve witnessed firsthand how complacency and the unwillingness to adapt to new instruments has rendered tech workers out of date.
# 2. Being Paid to Be taught
At my present full-time job, there are coaching programs led by AI specialists that train you to combine LLMs into your information science workflows. Common hackathons with groups like information and software program engineering permit you to acquire talent units that transcend your scope of labor. There are peer-led tutorial periods nearly each week the place different staff members stroll you thru an issue they solved and present you how you can construct the same undertaking. This protects a ton of time and teaches you excess of most on-line programs.
A full-time job is the one place the place you study on any person else’s dime, as an alternative of getting to enroll your self in a $1,000 bootcamp.
After I targeted solely on freelance work, two issues occurred:
- Firstly, I wasn’t incentivized to study new issues except a consumer had an issue that required me to upskill.
- If I did must study one thing new, I usually paid for an internet course.
And if I obtained caught or did not perceive one thing, I did not have anybody round who might assist me grasp the idea.
3. AI-Proofing My Profession
This is perhaps controversial to some, however the greatest purpose I obtained a full-time information science job is as a result of I imagine it is going to assist safe my profession from AI. And whereas this may sound counterintuitive, hear me out.
With my freelance job, this is what I realized:
- Easy methods to use my present abilities to unravel the consumer’s downside
- Gathering consumer necessities and utilizing them to unravel a particular technical difficulty
Nevertheless, with a full-time job at a big tech firm, my scope now entails:
- Gathering a enterprise requirement and dealing with groups like product, design, and engineering to show it into an information downside
- Making key product selections
- Understanding how the corporate’s information warehouse works and utilizing it to construct information pipelines
- Constructing relationships with stakeholders and friends
With freelance work, you usually resolve a focused technical downside for the corporate — akin to constructing a dashboard and refreshing it each quarter, or making a machine studying mannequin for a particular use case. The necessities are clearly specified, and also you simply must deal with execution together with your technical abilities.
Nevertheless, AI is democratizing technical abilities.
It permits individuals who do not know how you can code to construct functions. Individuals who do not know SQL can simply write a question and create a complete dashboard. As AI continues to democratize technical abilities, the worth of information science freelancers will possible decline. The pay will lower, and the area will change into extra aggressive.
Conversely, a company position is multifaceted. It requires much more collaboration, area experience, essential considering, and understanding of the enterprise. As you climb the information science company ladder and attain greater positions throughout the firm, you may change into tougher to exchange (whilst AI fashions get higher). Additionally, you’ll be able to transition to roles like enterprise analyst or product supervisor and even negotiate greater salaries. To place it merely, there are a lot of methods to maneuver ahead in a company position. You possibly can oversee information options and drive enterprise worth in ways in which do not overlap with AI’s capabilities.
Then again, working a contract job the place the one worth you convey is your technical talent places you in a weak place.
For that purpose, I’ve determined to prioritize long-term profession security over short-term revenue. I selected a lower-paying full-time job over freelance information science roles to construct a set of abilities that can hold me related within the subsequent decade, no matter how AI impacts the technical facet of the career.
Abstract
To summarize, I give up my snug, high-paying freelance roles to take a way more demanding full-time information science job. And I did it for the next causes:
- To study technical abilities at a sooner tempo
- To climb the company ladder and prioritize long-term monetary stability over short-term revenue
- To safe my profession from AI by gaining expertise and studying abilities that can not be changed (akin to enterprise and product data, stakeholder administration, and important considering)
YMMV, nevertheless, so I encourage you to do your individual analysis. Drop a remark under for those who really feel you might have beneficial perception for others.
 
Natassha Selvaraj is a self-taught information scientist with a ardour for writing. Natassha writes on all the things information science-related, a real grasp of all information matters. You possibly can join along with her on LinkedIn or take a look at her YouTube channel.
