Starting a Career in Data Science | Massimiliano Ungheretti | Beyond Coding Podcast #142

Evolution

Massimiliano Ungheretti discusses the evolution of data science, highlighting significant changes in tools and cloud technology. He notes that while cloud adoption is not as widespread as assumed, it has transformed workflows, reducing reliance on manual processes like Excel sheet updates 1. Massimiliano also emphasizes the shift from requiring PhDs to more accessible roles due to off-the-shelf algorithms, making the field less academically demanding 1.

The job became a bit easier in that respect, like less academic, because there's way more things you can take off the shelf that makes, I think, all the difference.

--- Massimiliano Ungheretti

He further explains his involvement in generative AI projects, such as developing a voice assistant for cars, which showcases the dynamic opportunities within data science today 2.

   

Roles

The roles within data science have become more specialized, moving away from the catch-all title of "data scientist." Massimiliano Ungheretti describes how companies now differentiate between data engineers, machine learning engineers, and research scientists, among others 3. This specialization helps clarify job expectations, although entry-level positions may still use broader titles.

The profiles that companies are looking for are becoming a little bit clearer than they were a couple of years ago.

--- Massimiliano Ungheretti

Massimiliano shares his journey from academia to industry, where he initially worked on machine learning for physics before transitioning to a data scientist role at ING. His experience underscores the importance of combining programming, math, and data science skills to succeed in the field 4.