The 3 Core Careers in AI
Updated: Oct 7
There are three core highly technical roles in this space.
The explosion in data science has created and redefined several careers. Data science and machine learning are relatively new careers in the applied space. The range of candidates for data science positions has grown to include computer scientists, mathematicians, and physicists as well as business school graduates, economists, and other social scientists. Role confusing is rampant in this space.
Currently, there are three top tier roles within the data science space, they are the data scientist, the machine learning engineer and the data engineer.
The most famous of these roles in the data scientist. The data scientist role has academic origins; thus, many data scientists have solid math and statistics skills, and many will have advanced degrees. They have business acumen and analytical skills as well as the ability to mine, clean, and present data.
However, they are often weak in many of the real world skills such as data sourcing and programming. I believe this weakness will lead to a dramatic scaling back of the data science role in the applied space.
The second role is the machine learning engineer. Machine learning engineers use programming languages to work through the machine learning process. The default language for working with data in the applied space is SQL and it’s often one of the top skills companies look for in their machine learning engineers. Machine learning engineers build models that make predictions. Two programming languages used often in the applied space are Python and R. Machine learning engineers will often be responsible for performance, optimization and aiding in deploying the final model to production. The machine learning engineer's weakness is often statistics and mathematics.
The third role is that of the data engineer. Data engineers manage large amounts of changing data in varying formats. Most companies have structured data, like data housed in relational databases and unstructured data, like text files sitting on a file system. The data engineer focuses on the development, deployment, management, and optimization of data pipelines and infrastructure. Data engineers are concerned with the production readiness of data and all that comes with it: formats, scaling, resilience and security. From a skills perspective, data engineers will be familiar with operating systems, SQL, Big Data technologies, storage systems and data ingestion tools. They are often the company’s data stewards.
There is one role that’s often confused with the data scientist and that is the role of the data analysts.
In the applied space, a data analyst is someone who uses SQL or other third-party tools to create reports. Often, this role sits outside of information technology.
The scope of technical skills required to be a data analyst isn’t the same as those required for data science, machine learning or data engineering. However, the data analyst role provides you with the SQL skills you'll need to begin your journey in information technology. The hardest part for many new to machine learning will be securing their first IT role. Machine learning engineers and data engineers are highly technical roles that will require a vast array of knowledge and skills.