Definitions are fuzzy, as are skill requirements
There are many ways to define data science. The most popular seems to be that data science lies at the intersection of computer science, mathematics and statistics, and domain knowledge.
It is always easy to criticize the commonly used Venn diagram above. However, keep in mind that they are intentionally oversimplified and therefore naturally imperfect. Personally, I think this is a useful way to conceptualize data science. If your job involves computer science (programming, databases, cloud infrastructure), mathematics and statistics (statistics, stochastics, machine learning) and domain knowledgeall to a non-trivial extent, you probably do data science.
Data scientists do very different things in practice
The problem is that this definition is very general. I met data scientists who…
- are unable use fundamental programming tools or techniques for their analyzes
- to have Never trained a machine learning model
- are isolated real business, focusing primarily on data pipelines or performance optimization
However, I met…
- Software engineers that train machine learning models
- Data analysts who build complex data pipelines using Python
- Business Analysts who use advanced statistical models but have never thought of it as AI
Jobs related to data science can be quite confusing in the real world because…
- There is significant skill overlap between similar roles (data analyst, data engineer, data scientist, machine learning engineer, AI engineer)
- Companies define these roles differently depending on their industry and size.
- People take on new responsibilities but stay in the same position, never changing their job title.
- Job requirements for the same role change quickly
If you are able to extract data from a data warehouse using SQL and visualize statistical information using Python, this would have secured you a great job as a data scientist 10 years ago. Nowadays, you may still have a chance in a traditional organization like a large insurance company. However, if you try to join a unicorn tech startup As a data scientist, you have a better understanding of how to train ML models, deploy them in the cloud, and set up monitoring and recycling mechanisms with versioning of data, models, and code. If you have 10+ years of experience using ChatGPT, that’s another plus.
Find your path to personal development
I think the main conclusions from these observations are that you should focus your personal skills development on what brings business valuenot what some arbitrary definition of your current job title requires.
If you solve relevant business problems, enjoy your work and are well paid, don’t worry about what others think the market demands of you.
Of course, you should strive to expand your skillset, and in today’s world, staying in the same role at the same company for 10 years is rarely optimal for long-term skills progression. But if you have found a business niche in which your personal skills are highly valued, you can be sure that there is other companies with the same problem. Your job is to make sure you can solve this problem, now and in the future.
Comparing yourself to others can be helpful, but also distracting. Others have different personalities and interests and probably do a completely different job than you. Programming, Machine Learning, cloud platforms, etc. are just tools. Learn the tools you actually need to solve a specific business problem.
