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. Introduction
When I first started my data science career in 2020, the field was on the rise. Wherever you saw, companies were hiring data professionals. At that time, I created a data science portfolio and managed to land several high -paying clients.
I will write data science content, such as white papers, articles, and technical documents – Which payment is between USD $ 500 and \ $ 1,000 For two days of work. I created simple models of machine learning and analyzed using tolls like tables and power BI. Since the client started recommending my work and leaving positive reviews, I took off more plans. I worked 5 to 6 hours every day with my sofa and was completely remote.
Recently, however, I have changed things.
I left some freelance jobs for full -time data science position. And no, it’s not because the job pays more. In fact, I have earned more money as a freelance data scientist.
So why did I change the full -time position from a comfortable, high -paying freelance job that pays less?
Read and you will know the three high concerns that I took this action.
. 1. Technical skills build up
When I worked for myself, I realized that I would kill once in learning technical skills. I was working more like a machine, producing repeated results for the same freelance clients. This meant that I not only did less work, but my technical knowledge was stopped.
One of the facts was when I attended a well -known tech conference and network with other data professionals. I realized that I had not kept most technology on which they discussed. These data were building a generation (RAG) system more than professional AI agents and recovery, while I was refreshing the same dashboard for the hundredth time and writing white papers for data science.
Don’t misunderstand me – the value of a data scientist is in their results, and in many cases, large language models (LLMs) such as fancy tools are equivalent to using a slap hammer to break the nut. However, I lacked basic information about the tools that were at the forefront of the tech companies, and it frightened me. I have observed myself how prosperity and dislikes for adoption of new tools have made tech employees obsolete.
. 2. Payment is being made to learn
At my current full -time job, there are training courses headed by AI experts that teach you to integrate LLM into your data science workflows. Regular heckathan with teams like data and software engineering allows you to get a set of skills that go beyond your work scope. Almost every week there are peer -led tutorial sessions where other team members suffer from a problem and show you how to build a similar project. This saves a ton of time and teaches you more than most online courses.
A full -time job is a place where you learn on someone else’s dim, rather than entering themselves to $ 1,000 botamp.
When I focused focus on freelance work, there were two things:
- O.L, I was not encouraged to learn new things unless a client had any problems that I needed to do.
- If I had to learn something new, I usually paid for an online course.
And if I got stuck or didn’t understand anything, I had no one around that could help me understand this concept.
3. AI-Profaging My Career
This may be controversial for some people, but the main reason for getting a job of full -time data science is that I believe it will help protect my career from AI. And when it may seem contradictory, listen to me.
With my free job, what I learned is here:
- How to use my existing skills to solve the client’s problem
- To collect client requirements and use them to solve a particular technical problem
However, with a full -time job in a large tech company, now my scope includes:
- Working with teams such as products, design, and engineering to collect business needs and turn it into a data problem
- Key product decisions
- Understanding how the company’s data warehouse works and using it to build data pipelines
- To build relationships with stakeholders and colleagues
With freelance work, you usually solve a target technical problem for the company – such as making a dashboard and refreshing it every quarter, or making a machine learning model for a particular use issue. The requirements are clearly clarified, and you need to focus on implementing your technical skills.
However, AI technical skills have to be democratic.
This allows people who do not know how to make applications. People who do not know the SQL can easily write a question and create a comprehensive dashboard. Since AI continues to democratically, the value of data science freelancers will decrease. The salary will decrease, and the space will be more competitive.
On the contrary, a corporate character is multi -faceted. It requires more support, domain skills, critical thinking and understanding of business. When you climb the data science corporate ladder and reach high positions within the company, you become more difficult to change (even the AI ​​models improve). Also, you can transfer roles like a business analyst or product manager and even discuss high salaries. In straight words, there are many ways to move forward in the corporate role. You can monitor the data solutions and run the business price in ways that are not overlap with AI’s capabilities.
On the other hand, working on an independent job where you just get the price is your technical skills you keep in a weak position.
For this reason, I have decided to prioritize the safety of a long -term career than short -term income. I chose a full -time full -time job than freelance data science characters to create a combination of skills that will keep me relevant in the next decade, regardless of how AI affects the technical aspect of the profession.
Abstract
To summarize, I left my comfortable, high -paying freelance characters to demand full -time data science jobs. And I did it for the following reasons:
- Fast to learn technical skills.
- To climb the corporate ladder and prefer long -term financial stability over short -term income
- To secure your career by acquiring AI experience and learning skills that cannot be changed (such as business and product knowledge, stakeholder management, and critical thinking)
YMMV, however, so I encourage you to do my research. If you think you have valuable insights for others, leave a comment below.
& nbsp
Natasa Selorj Is a self -educated data scientist with a passion for writing. Natasa writes on everything related to science, which is a real master of all data titles. You can contact with it Linked Or check it out Utube channel.