Read this if you are a data scientist (or another professional) worried about your career.
So many people, including me, write about how fields such as copywriting, drawing, or data science change from being accessible to a niche of highly professional individuals to a mere commodity. I claim it’s a good thing, not only for humankind but for the individual professional. Since I know nothing about drawing, I’ll talk about data science.
I started working as a data scientist a long time ago, even before the term data science was coined. Back then, my data science job included:
- writing code that implements this optimization algorithm or the other
- writing code that implements this statistical analysis or the other
- writing code that implements this machine learning technique of the other
- writing code that implements this quality metric or the other
- writing code that handles named columns
- writing code that deals with parallelization, caching, fetching data from the internet
Back then, exactly when the term data scientist was coined, I used to say “data is data”. I claimed that it didn’t matter whether you write a model that detects cancer or detects online fraud, a model that simulates two molecules in a solution or a model that simulates players in the electric appliances market. Data was data, and my job, as a data scientist was to crunch it.
Time passed by. Suddenly, I discovered one cool library, the other, and a third one … Suddenly, my job was to connect these libraries, which allowed me to be more expressive in what I could achieve. It also allowed me to concentrate better on “business logic.” Business logic is the term I use to describe all the knowledge required for the organization that pays your salary to keep doing so. If you work for a gaming company, “business logic” is the gaming psychology, competitor landscape, growth methods, and network effect. If you work for a biotech company, “business logic” is the deep understanding of disease mechanisms, biochemistry, genetics, or whatever is needed to perform the breakthrough. The fact that I don’t need to deal with “low-level coding” made me obsolete and drove me to a state where I became more specialized.
These days, we are facing a new era in knowledge commoditization. This commoditization makes our skills obsolete but also makes us more efficient in tasks that we were slow at and lets us develop new skills.
In 2017, Gartner predicted that more than 40% of data science tasks would be obsolete by 2020. Today, in 2023, I can safely say that they were right. I can also say that today, despite the recent layouts, there are much more busy data scientists than there were in 2017 or 2020.
The bottom line. Stop worrying.
Let me cite myself from 2017:
Data scientists won’t disappear as an occupation. They will be more specialized.
I’m not saying that data scientists will disappear in the way coachmen disappeared from the labor market. My claim is that data scientists will cease to be perceived as a panacea by the typical CEO/CTO/CFO. Many tasks that are now performed by the data scientists will shift to business developers, programmers, accountants and other domain owners who will learn another skill — operating with numbers using ready to use tools. An accountant can use Excel to balance a budget, identify business strengths, and visualize trends. There is no reason he or she cannot use a reasonably simple black box to forecast sales, identify anomalies, or predict churn.
This is another piece of career advice. I have more of them in my blog