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Obvious Introduction
When I first started my scientific work in 2020, the field was up. Every place where he looked, companies were hiring data experts. At that time, I develop a data science portfolio and I was able to reach a few high pay customers.
I can write the content of the data science, such as white sheets, articles, and technical texts – Payment between USD $ 500 and $ 1,000 two days of work. I have built a simple machine learning models and analysis of analytical system using tools such as Tableau and BI. As clients begin to commend my work and leave good reviews, I arrived many projects. I worked 5 to 6 hours a day on my bed and I'm completely away.
Recently, though, I've changed things up.
I have stopped a few jobs of full scientific degree of science scientific – one where I go to the office every day and work twice hours. And no, it isn't because the job pay more. In fact, I make a lot of money as a Freelance Data Scientist than I do now.
So why did I change at a good, most paying event paying a lot in a full-time payable situation?
Learn and you will find the highest maximum anxiety that led me to taking this action.
Obvious 1. Creating technical skills
When I worked for myself, I realized that I had a plain by learning technical skills. I was very active as a machine, producing the repeated recent clients of the best customer. This was meant I didn't work a little, but my technical knowledge has reached ends.
Reality tests came when I went to the famous technical conference and interacted with other data experts. I realized that I did not continue the many technologies they talk to. These data authorities built AIs and the retreeked generation green green green green (CAG), while I kiss the same dashboard for 100 times and to write white psychological papers.
Don't mistake me – the number of scientists are in the consequences they are driving, and in many cases, sweet tools are like a large model of languages (llms) Akinking usage nuts. However, I did not have a basic knowledge of tools for technical, and frightening companies. I personally have to develop how to be glorious and not to adapt to new tools and give technical workers.
Obvious 2. Paid to read
In my current full-time workplace, there are training-led training lessons that teach you llms in your scientific science transit. Standard Hackathons have groups such as data and software engineers allow you to get the skills for more than your work limit. There are times of peer educational times a week when other group members go about the problem they have resolved and show you how to build the same project. This saves the time tone and teaches you much more than many online learning.
The full-time work is one place where you read someone else's university, instead of registering $ 1,000 bootcamp.
When I focus more in the size of the size, two things happened:
- First, I didn't encourage to read new things unless the client had a problem that he wanted to go up.
- If I should learn something new, I was paying for an online course.
And if I'm caught up or I don't understand something, I didn't have someone close to me can help me understand your mind.
3. Ai-Proafung my work
This can be an issue for others, but the greatest reason I have received is the full-time science is because I believe it will help to protect my work from Ai. And while this would be united, I heard.
For my independent work, here is what I read:
- How can you use my existing skills to solve the client problem
- Collecting customer needs and use yourself to solve a particular technical problem
However, with a full-time work for a large technical company, my number now includes:
- Collecting a business need and applies to groups such as product, make-up, and engineering to convert to data problem
- Making Important Product Decisions
- How to understand how Company Data Werehouse works and uses it to build data pipes
- Build Relationships with Participants and Peer
With Freelance work, you often solve the technical problem addressed by the company – such as building dashboard and refreshes it in quarter, or to create a specific use model of use. Needs are clearly defined, and you just need to focus on your expert skills.
However, AI is technical technical skills.
Allows people to do not know which code to build apps. People who do not know SQL can easily write the question and create a complete dashboard. As AI continues to promote technical technical skills, the number of data freelelancers may decrease. Pay will decrease, and space will be more competitive.
On the other hand, the organization's role in Multifaceted. It requires the closest partner, domain technology, sensitive thinking, and business comprehension. As you climb a Data Science Science Laddder and reach the higher positions inside the company, you will be more difficult to replace (even the better AI models). Also, you can change the roles like a business commentator or product manager and negotiate high pay. Just putting, there are many ways to move on to the organization's passage. You can view the data solutions and drives the business value in ways that no AI's skills do not.
On the other hand, working on a prideful work where the only number you are doing is your technical skill to put you in a vulnerable place.
For that reason, I have decided to prioritize the safety of skills for a long time in temporary income. I have chosen a full-time job of full-time on Freelance and how to build the skills set of skills that will keep it appropriate for the next decade, regardless of the AI process.
Summary
To summarize, I stopped my free roles, which paid the paid to take the most demanding work of full-time data. And I've done it for the following reasons:
- In order to learn technical skills at fast speed
- Climbing a business ladder and sets forward financial stiffness for a long time for short-term revenue
- Protecting my work from AI for acquiring skills and unchanged learning skills (such as business information and product information, stakeholder management)
YMMV, however, so encourage you to do your own research. Pull Comments below if you feel that you have an important understanding of others.
& Nbsp
Natassha Selvaraj You are a familiar data scientist for writing. Natassha writes in every science related to scientific, related to the actual king of all data topics. You can connect with him in LinkedIn or evaluate his YouTube station.
