Picture by Writer | Ideogram
# Introduction
Once I first began my information science profession in 2020, the sphere was booming. In every single place you appeared, 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’d write information science content material, comparable 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 shoppers began recommending my work and leaving constructive critiques, 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 stop a couple of freelance jobs for a full-time information science place — one the place I’m going to the workplace day by day and work double the hours. And no, it is not as a result of the job pays extra. Actually, I made more cash as a contract information scientist than I do now.
So why did I swap 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 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 test got here once I attended a good tech convention and networked with different information professionals. I noticed I hadn’t saved up with a lot of the expertise they mentioned. These information professionals have been constructing AI brokers and retrieval-augmented era (RAG) methods, 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 — 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. Nevertheless, 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 Study
At my present full-time job, there are coaching programs led by AI consultants that train you to combine LLMs into your information science workflows. Common hackathons with groups like information and software program engineering can help you acquire talent units that transcend your scope of labor. There are peer-led tutorial periods virtually each week the place different staff members stroll you thru an issue they solved and present you construct the same challenge. This protects a ton of time and teaches you way over 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 shopper had an issue that required me to upskill.
- If I did need to study one thing new, I usually paid for a web based course.
And if I received 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 received a full-time information science job is as a result of I consider it’ll assist safe my profession from AI. And whereas this may sound counterintuitive, hear me out.
With my freelance job, here is what I discovered:
- The best way to use my present expertise to resolve the shopper’s downside
- Gathering shopper necessities and utilizing them to resolve a particular technical subject
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 — comparable 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 expertise.
Nevertheless, AI is democratizing technical expertise.
It permits individuals who do not know 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 seemingly decline. The pay will lower, and the area will change into extra aggressive.
Conversely, a company function 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 greater positions throughout the firm, you may change into tougher to interchange (at the same time as AI fashions get higher). Additionally, you possibly can 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 deliver is your technical talent places you in a susceptible place.
For that cause, 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 can maintain me related within the subsequent decade, no matter how AI impacts the technical facet of the occupation.
Abstract
To summarize, I stop 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 quicker 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’t be changed (comparable to enterprise and product information, stakeholder administration, and demanding considering)
YMMV, nevertheless, so I encourage you to do your individual analysis. Drop a remark under in case you really feel you might have invaluable perception for others.
 
Natassha Selvaraj is a self-taught information scientist with a ardour for writing. Natassha writes on all the pieces information science-related, a real grasp of all information subjects. You’ll be able to join along with her on LinkedIn or try her YouTube channel.
Picture by Writer | Ideogram
# Introduction
Once I first began my information science profession in 2020, the sphere was booming. In every single place you appeared, 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’d write information science content material, comparable 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 shoppers began recommending my work and leaving constructive critiques, 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 stop a couple of freelance jobs for a full-time information science place — one the place I’m going to the workplace day by day and work double the hours. And no, it is not as a result of the job pays extra. Actually, I made more cash as a contract information scientist than I do now.
So why did I swap 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 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 test got here once I attended a good tech convention and networked with different information professionals. I noticed I hadn’t saved up with a lot of the expertise they mentioned. These information professionals have been constructing AI brokers and retrieval-augmented era (RAG) methods, 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 — 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. Nevertheless, 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 Study
At my present full-time job, there are coaching programs led by AI consultants that train you to combine LLMs into your information science workflows. Common hackathons with groups like information and software program engineering can help you acquire talent units that transcend your scope of labor. There are peer-led tutorial periods virtually each week the place different staff members stroll you thru an issue they solved and present you construct the same challenge. This protects a ton of time and teaches you way over 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 shopper had an issue that required me to upskill.
- If I did need to study one thing new, I usually paid for a web based course.
And if I received 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 received a full-time information science job is as a result of I consider it’ll assist safe my profession from AI. And whereas this may sound counterintuitive, hear me out.
With my freelance job, here is what I discovered:
- The best way to use my present expertise to resolve the shopper’s downside
- Gathering shopper necessities and utilizing them to resolve a particular technical subject
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 — comparable 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 expertise.
Nevertheless, AI is democratizing technical expertise.
It permits individuals who do not know 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 seemingly decline. The pay will lower, and the area will change into extra aggressive.
Conversely, a company function 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 greater positions throughout the firm, you may change into tougher to interchange (at the same time as AI fashions get higher). Additionally, you possibly can 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 deliver is your technical talent places you in a susceptible place.
For that cause, 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 can maintain me related within the subsequent decade, no matter how AI impacts the technical facet of the occupation.
Abstract
To summarize, I stop 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 quicker 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’t be changed (comparable to enterprise and product information, stakeholder administration, and demanding considering)
YMMV, nevertheless, so I encourage you to do your individual analysis. Drop a remark under in case you really feel you might have invaluable perception for others.
 
Natassha Selvaraj is a self-taught information scientist with a ardour for writing. Natassha writes on all the pieces information science-related, a real grasp of all information subjects. You’ll be able to join along with her on LinkedIn or try her YouTube channel.
