The growing amount of fake job ads in data science and AI industry sectors is an indication of incompetent data scientists where job ads whose job titles don't match the job description. Job ad saying it is 'AI Engineer' that doesn't require any AI skills, then why call it an AI Engineer role? Such practices need to stop. Usually, an indication of fake job ads is when they mention that you will be working along side data scientists. Either the company does not know what they are doing or they don't know that AI Engineers are more than capable of doing the data scientists job. In fact, one will find that even job ads that mention machine learning engineers are simply about supporting some clueless data scientist whose job should include being able to pipeline and scale out their own work. Don't hire incompetent data scientists and waste your time having to create new roles to fill all their skills gaps and not only that but the new roles are fake where the job titles don't match the job descriptions. Such practices only lead to recruitment fraud, big teams where roles have significant overlaps, and unnecessary cost to organizations.
Similar indication of clueless hiring processes where they could simply call it a software engineer role:
- Computer Vision Engineer - where they don't need computer vision skills because clueless data scientists are building their models in jupyter notebooks
- NLP Engineer - where they don't need NLP skills because clueless data scientists are building their models in jupyter notebooks
- ML Engineer - where they don't need ML skills because clueless data scientists are building their models in jupyter notebooks
- Ontologists - who need KG Engineers to help them build a pipeline, to do integration between ontologies, to help you build the codified ontologies, and everything that would make you wonder why you even bothered to hire an ontologist in first place?
- Researchers - who need software engineers to help them scale out the work of their hacked out artefacts, who make you wonder why you bothered to hire a researcher with a Phd who hasn't even got the basic skills?
- Data Engineers - who are needed to help with 80% of the data scientists job like feature engineering, cleaning data, ETL, pipelining...who make you wonder why you bothered to hire a data scientist?
Essentially, any data scientist that is only interested in building models is a clueless data scientist:
- they are only capable of doing 20% of their job
- the models they build are invariably overfitted to the data because someone else is doing feature engineering for them
- they don't understand that they are responsible for the entire data science method including pipeline and scale out of their work into production
- you need to keep hiring and creating new roles to fill all their skills gaps like data engineer, mlops engineer, and so on
- especially, if they are building a deep learning model
- and, if you are having to fill all their roles not only are you stuck creating fake job ads but at same time they provide for a lot of skills overlaps in teams
Why not then simply either remove the role of the data scientist/researcher, or simply hire more capable people and save up on cost of hiring for incompetence where academics does not replace practical skills. Such practices will only get worse in organizations if they are seeking advice from Phd individuals who simply have academic backgrounds and limited practical skills. And, in most organizations practical skills should trump over academic backgrounds when costs and revenues are important for a business to meet profitability targets.
