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# Introduction
Once I first began my information science profession in 2020, the sphere was booming. In every single place you regarded, firms have been hiring information professionals. At the moment, I constructed a knowledge science portfolio and managed to land a number of high-paying shoppers.
I might write information science content material, reminiscent of 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 shoppers began recommending my work and leaving optimistic critiques, I landed extra initiatives. I labored 5 to six hours every day from my sofa and was fully distant.
Not too long ago, nonetheless, 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’m going to the workplace on daily basis and work double the hours. And no, it isn’t as a result of the job pays extra. In actual fact, 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 prime considerations that led me to taking this motion.
# 1. Constructing Technical Expertise
Once I labored for myself, I noticed I would hit a plateau in studying technical expertise. I used to be working extra like a machine, producing repetitive outcomes for a similar freelance shoppers. This meant that I not solely labored much less, however my technical information had reached a standstill.
A actuality verify got here once I attended a good tech convention and networked with different information professionals. I noticed I hadn’t stored up with a lot of the know-how they mentioned. These information professionals have been constructing AI brokers and retrieval-augmented era (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 unsuitable — a knowledge 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. Nonetheless, I lacked primary information 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 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 mean you can acquire ability units that transcend your scope of labor. There are peer-led tutorial periods virtually each week the place different workforce members stroll you thru an issue they solved and present you the right way to construct an analogous 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 be taught on any person else’s dime, as a substitute 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 be taught new issues except a consumer had an issue that required me to upskill.
- If I did need to be taught one thing new, I sometimes 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 may be controversial to some, however the largest 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 discovered:
- Learn how to use my current expertise to unravel the consumer’s downside
- Gathering consumer necessities and utilizing them to unravel a particular technical subject
Nonetheless, 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 sometimes clear up a focused technical downside for the corporate — reminiscent of 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 have to deal with execution together with your technical expertise.
Nonetheless, AI is democratizing technical expertise.
It permits individuals who do not know the right way to code to construct purposes. 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 knowledge science freelancers will probably decline. The pay will lower, and the house will turn out to be extra aggressive.
Conversely, a company function is multifaceted. It requires way more collaboration, area experience, crucial pondering, and understanding of the enterprise. As you climb the info science company ladder and attain greater positions inside the firm, you will turn out to be tougher to exchange (at the same time as 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 numerous methods to maneuver ahead in a company function. You’ll be able to oversee information options and drive enterprise worth in ways in which do not overlap with AI’s capabilities.
Alternatively, working a contract job the place the one worth you convey is your technical ability places you in a weak place.
For that purpose, I’ve determined to prioritize long-term profession security over short-term earnings. I selected a lower-paying full-time job over freelance information science roles to construct a set of expertise that may preserve me related within the subsequent decade, no matter how AI impacts the technical aspect 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 be taught technical expertise at a sooner tempo
- To climb the company ladder and prioritize long-term monetary stability over short-term earnings
- To safe my profession from AI by gaining expertise and studying expertise that can’t be changed (reminiscent of enterprise and product information, stakeholder administration, and significant pondering)
YMMV, nonetheless, so I encourage you to do your personal analysis. Drop a remark beneath should you really feel you’ve precious perception for others.
 
Natassha Selvaraj is a self-taught information scientist with a ardour for writing. Natassha writes on every thing information science-related, a real grasp of all information matters. You’ll be able to join along with her on LinkedIn or try her YouTube channel.