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# Introduction
Once I first began my information science profession in 2020, the sector was booming. In all places you seemed, firms have been hiring information professionals. At the moment, I constructed a knowledge science portfolio and managed to land a number of high-paying purchasers.
I’d write information science content material, corresponding 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 carried out analyses utilizing instruments like Tableau and Energy BI. As purchasers began recommending my work and leaving constructive evaluations, I landed extra tasks. I labored 5 to six hours every day from my sofa and was utterly distant.
Not too long ago, nevertheless, I’ve modified issues up.
I’ve stop 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 reality, I made more cash as a contract information scientist than I do now.
So why did I change from a cushty, high-paying freelance job to a full-time place that pays much less?
Learn on and you will find out the three prime considerations that led me to taking this motion.
# 1. Constructing Technical Abilities
Once I labored for myself, I spotted I might hit a plateau in studying technical expertise. 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 saved up with a lot of the know-how they mentioned. These information professionals have 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 mistaken — a knowledge scientist’s worth is within the outcomes they drive, and in lots of instances, 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 have 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 Study
At my present full-time job, there are coaching programs led by AI consultants that educate you to combine LLMs into your information science workflows. Common hackathons with groups like information and software program engineering will let you achieve talent units that transcend your scope of labor. There are peer-led tutorial periods nearly each week the place different crew members stroll you thru an issue they solved and present you how you can construct an identical challenge. 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 individual else’s dime, as an alternative of getting to enroll your self in a $1,000 bootcamp.
Once I centered solely on freelance work, two issues occurred:
- Firstly, I wasn’t incentivized to study new issues until a shopper had an issue that required me to upskill.
- If I did should study one thing new, I usually paid for a web based course.
And if I bought 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 largest motive I bought a full-time information science job is as a result of I consider it’s 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 discovered:
- Easy methods to use my present expertise to resolve the shopper’s downside
- Gathering shopper necessities and utilizing them to resolve a selected technical problem
Nevertheless, with a full-time job at a big tech firm, my scope now includes:
- Gathering a enterprise requirement and dealing with groups like product, design, and engineering to show it into a knowledge 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 remedy a focused technical downside for the corporate — corresponding to constructing a dashboard and refreshing it each quarter, or making a machine studying mannequin for a selected use case. The necessities are clearly specified, and also you simply must deal with execution together with your technical expertise.
Nevertheless, AI is democratizing technical expertise.
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 expertise, the worth of information science freelancers will probably decline. The pay will lower, and the house will change into extra aggressive.
Conversely, a company position is multifaceted. It requires much more collaboration, area experience, important considering, and understanding of the enterprise. As you climb the info science company ladder and attain increased positions inside the firm, you may change into harder to interchange (whilst AI fashions get higher). Additionally, you’ll be able to transition to roles like enterprise analyst or product supervisor and even negotiate increased salaries. To place it merely, there are lots of methods to maneuver ahead in a company position. You may 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 carry is your technical talent places you in a susceptible place.
For that motive, 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 expertise that may hold me related within the subsequent decade, no matter how AI impacts the technical aspect of the career.
Abstract
To summarize, I stop 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 expertise 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 expertise that can not be changed (corresponding to enterprise and product data, stakeholder administration, and demanding considering)
YMMV, nevertheless, so I encourage you to do your individual analysis. Drop a remark under if you happen to really feel you could 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 may join together with her on LinkedIn or take a look at her YouTube channel.
