In the tech world, two big trends already happen in these two years:

1. massive layoff and hiring freeze.
2. boosting advancement with GenAI, like chatGPT.

Those two things seem related, and I was shocked and afraid in the beginning. I have even thought of changing my career to data science. I started to learn, deep dive into the industry, and afterwards, made my decision about how to move on.

In this article, I will share my thoughts about: as a fullstack developer, how to face the impact of AI.

What does data scientist do in a company

It sounds so fancy and complicated. But to my understanding, for the product team, the main output of data science team is only data.

My words sound funny. But it's real: based on the existing data, data science team will use machine learning algorithm to analysis and produce new data to use or display.

For example, as a B2C website, the objectives of data science team will be: display the most customized content for a customer.

Why there never will be massive hiring for data scientist in a traditional IT company, at least in France

1. Data scientist needs to find opportunities from existing data. The opportunities are limited.
2. Data scientist team only produce data, but the work is not finished, because the company still need people to use those data, for example, display them to the client side.

Thus to me, data science team will never hire the same amount of people as engineering team (unless AI is able to code directly in the future), because data scientist is only to find potential opportunity. Data science to engineering, is a bit like UX to UI designer. It's a luxury for many companies.

Whether it worths to change the career path from fullstack developer to data scientist

It depends on your passion. If you are really passionate about AI: deep diving into the data and dare to learn countless new things, then it should be a good way to go.

However, if the motivation is purely out of fear (like me before) or gaining more money, then you might meet some difficulty.

Why ?

1. There are so many new things to learn. As a developer, it was not that difficult to change a language to code, but data scientist is far more than that.

   As for coding language, there are python, and machine learning libraries.

   As for tools, there are Jupyter, steamlit, snowflake, AWS, Kedro, Airflow to learn if you want to reach the industrial level.

   The coding language and tools are 'learnable', but the real complicated part is the 'philosophy', or 'which algorithm / method to use when facing such data for such opportunity'. Those 'internal strength' is more difficult to learn than 'external strength'.

2. Like what has happened to fullstack developers, today companies rarely hire junior data scientist, because the learning curve is so long and companies want to hire someone who can produce outcome right away. It makes learning inside industry more difficult.
3. Even though you are a senior data scientist, there is no 'more' opportunity than senior fullstack developer, because it's a special and even luxury role to companies.

The future relationship between frontend, backend, data science

Web (frontend) developer and Backend developer roles are more easily to be merged when they use the same coding language (javascript). For example, if the web is SSR (server side rendering), the web developers need to implement the server part with NodeJS, it's already not far from backend developer.

If a frontend developer could learn SQL, Graphql, how rabbitmq works and Domain Driven Design, then he / she could already work for the backend, and then it's a problem of accumulating the experiences.

However, the knowledge gap between a data scientist and a backend developer is so huge,not only they don't share the same language, but also they don't share the same workflow and tool. The only connection point between data scentist and backend developer is data: how to produce and use those data.

Thus, in the future, as it is as for today, there might be a tendance to hire more senior fullstack developer than only front/back developer, unless the profiles are really good to handle specific issues, but it's quite rare to have a role of 'data scentist full stack developer'.

The real impact of AI to our career: what should we do as fullstack developer

Even though the rise of concern of data science will not reduce the number of classic developer jobs, AI still has a huge impact on our career as fullstack developers.

Github Copilot, ChatGPT can upgrade a developer's level so easily, a junior could be senior, and senior could.. still be senior in case of pure technical issues. Github Copilot and ChatGPT have raise the standard of engineering level in the whole industry. In the future, the company doesn't need so many developers to build a feature, they only need the senior ones with the help of Copilot and ChatGPT. They don't hire junior roles anymore, because there are so many supplies in the market !

Two insights from this:

• Finding an engineering job is not as easy as before. The industry will be normalized, there is less crazy legend, there is less mobility of talents， and there will be more bureaucracy and human factors.
• You have to be really good with your technical skills to find a job, since the standard of the whole industry has raised the bar.

In short, don't panic about data science, but do panic a bit with the bots who help you code.

Thanks for reading !