First, congrats Raptors! We The North! This is a great segway into the subject of a dream team.

I always find a little funny how many "data scientists" there are these days on LinkedIn compared to five years ago. What changed? Some are people re-branding themselves, while others genuinely possess a mix of specialized skills that are hard to come by in a single person. However, getting value from AI and big data is no longer a luxury; it's needed to remain competitive. All the competition is doing it. In the face of the fact that probably few of those who call themselves data scientists are the real deal, the solution is to create squads of people who, together, make up all the skills required to create value from data. So what are those skills or roles that are easier to staff?

First, are Solution Architects who design the data flows, ascertain how your applications are linked, and decide how to get best the job done. They are crucial to getting a good work plan.

Then we have the Data Engineers who develop the data pipelines and create the flow of data from one database to another. They have ETL skills as well as data modeling, write SQL, and are well-versed in all the data preparation and transformation technologies.

Data Analysts are similar to data detectives, people who can combine business acumen with data analysis skills. They are proficient at doing what it takes to answer precise business questions quickly.

Data Scientists investigate and develop machine learning models. They spend a lot of time organizing, cleansing, and wrangling data (where they can get help from the Data Engineers) and then running various machine learning models, evaluating their performance, and picking whichever works best for the particular business problem.

Machine Learning Engineers then take models developed by the Data Scientists and implement them at scale in production. The Machine Learning Engineers ensure that models are sturdy and efficient enough to run for a million transactions a day without fail.

All these roles have been around for a long time

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