Skip to content

Simplified AI-Data Talent Stack

With the overall resurgence of AI and the new generative AI capabilities that we have seen coming out primarily of OpenAI and their ChatGPT product, we thought it could be useful to provide a refresher on how we view the AI (and data) talent stack.

AI, and more specifically, machine learning (ML), are sophisticated statistical techniques that help with dynamic decision-making around optimizations, recommendations, forecasts, etc. Different from ML, basic statistical techniques that can be explained by a human, in some cases are enough to implement in processes or software products (i.e. linear regressions, etc). In other instances, more complex, black-box techniques, merit inclusion due to the nature or volume of the data, or time-sensitivity for decision-making.

In summary, machine learning algorithms (bundled inside the concept of artificial intelligence/AI) are trained with data. The data component is key here. For ML algos to work as trained, they need troves of data, and this is what connects the AI stack with the data stack.

In order to analyze data, a company needs to first source it, cleanse it/transform it, load it, etc. These tasks are typically performed by engineering roles: typically a Data Engineer, and for more senior roles, a Data Architect.

Once data is accessible in a data warehouse or data lake, Data Analysts or Data Scientists can start to analyze the data, come up with theses and try to find patterns. These professionals will use various techniques to help answer the business questions the company has.

As the team defines suitable models capable of learning and making predictions, they will need to operationalize these outside of R&D and into production. That’s when Machine Learning Engineers start to participate in the project by helping create the needed infrastructure that will perform as the business expects. Machine Learning (Deep Learning) Engineers could also be thought of as senior Data Scientists with strong software development and engineering training/skills.



In many cases there are no strong boundaries among these roles, but a data team with different professionals in diverse backgrounds collaborating with overlapped responsibilities looking to achieve the organization’s business goals.

At Alten Capital we enjoy the AI and data space. Please reach out to explore how we can collaborate.