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# Introduction
Once I first began my information science profession in 2020, the sphere was booming. In all places you appeared, corporations have been hiring information professionals. At the moment, I constructed an information science portfolio and managed to land a number of high-paying shoppers.
I might 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 shoppers began recommending my work and leaving optimistic evaluations, I landed extra tasks. I labored 5 to six hours every day from my sofa and was utterly distant.
Lately, nevertheless, I’ve modified issues up.
I’ve give up a couple of 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 is not 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 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 high issues that led me to taking this motion.
# 1. Constructing Technical Expertise
Once I labored for myself, I noticed 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 shoppers. 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 noticed 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 era (RAG) programs, 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 fallacious — an information scientist’s worth is within the outcomes they drive, and in lots of circumstances, fancy instruments like giant language fashions (LLMs) are akin to utilizing a sledgehammer to crack a nut. Nonetheless, I lacked primary data of instruments that have been on the forefront of tech corporations, and that scared me. I’ve witnessed firsthand how complacency and the unwillingness to adapt to new instruments has rendered tech staff out of date.
# 2. Being Paid to Study
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 let you achieve 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 mission. 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 a substitute of getting to enroll your self in a $1,000 bootcamp.
Once I targeted solely on freelance work, two issues occurred:
- Firstly, I wasn’t incentivized to study new issues until a consumer 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 acquired 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 greatest cause I acquired a full-time information science job is as a result of I imagine it would assist safe my profession from AI. And whereas this may sound counterintuitive, hear me out.
With my freelance job, here is what I realized:
- The way to use my current expertise to unravel the consumer’s drawback
- Gathering consumer necessities and utilizing them to unravel a selected technical subject
Nonetheless, 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 drawback
- 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 drawback for the corporate — akin 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 have to deal with execution along with your technical expertise.
Nonetheless, AI is democratizing technical expertise.
It permits individuals who do not know how you can 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 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 info science company ladder and attain larger positions throughout the firm, you will turn into harder 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 larger 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 weak place.
For that cause, 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 can maintain me related within the subsequent decade, no matter how AI impacts the technical aspect of the career.
Abstract
To summarize, I give up my comfy, 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 earnings
- To safe my profession from AI by gaining expertise and studying expertise that can’t be changed (akin to enterprise and product data, stakeholder administration, and demanding considering)
YMMV, nevertheless, so I encourage you to do your personal analysis. Drop a remark beneath in the event you really feel you could have helpful perception for others.
 
Natassha Selvaraj is a self-taught information scientist with a ardour for writing. Natassha writes on every little thing information science-related, a real grasp of all information subjects. You may join along with her on LinkedIn or try her YouTube channel.
