In the search for Machine Learning Engineer (MLE) roles, it’s becoming evident that a significant portion of these positions — though certainly not all — appear to favor candidates with PhDs over those with master’s degrees. LinkedIn Premium insights often show that 15–40% of applicants for such roles hold a PhD. Within large organizations, it’s also common to see many leads and managers with doctoral degrees.

This raises a concern: Is there an unspoken glass ceiling in the field of machine learning for professionals without a PhD? And this isn’t just about research or applied scientist roles — it seems to apply to ML engineer and standard data scientist positions as well.

Is this trend real, and if so, what are the reasons behind it?

If you’ve been in data long enough, you’ve probably made a few classic mistakes and seen others do the same. Maybe it was building a model without understanding the business context. Or skipping proper data cleaning. Or assuming the data was “good enough.”

What’s one common misstep that you learned the hard way  and now look out for in every project?
If you could give one piece of advice to someone just starting out in data science, analytics, or ML, what would it be?
Your experience could save someone else a lot of time, confusion, or even a failed project.

Let’s pass the torch and help the next wave of data experts grow smarter, faster.

With new AI tools launching almost daily, it’s easy to get overwhelmed by the noise. But let’s talk real life:
What’s one AI tool, platform, or workflow that you’ve actually used consistently and seen results from?

Maybe it helped you write SQL faster, generate code snippets, automate repetitive cleaning tasks, build better visuals, or summarize technical documents.
It doesn’t have to be fancy just something that’s genuinely made your life easier.
Share what you’re using, how you’re using it, and why you stuck with it. 

From finance to healthcare, marketing to manufacturing — ML and Data Science are no longer niche. They’re reshaping how decisions are made, how systems learn, and how businesses scale.

• Are people shifting because of demand, hype, or real value?
• What pulled you into this space?
• What trends do you think are driving this mass interest?

I’m just starting to learn SQL and I’m looking for some solid websites or apps that are beginner-friendly. Ideally, I’d like something interactive or hands-on rather than just reading theory. I’m not aiming to become a full-on data engineer—just want to get comfortable writing queries, understanding databases, and maybe do some small projects.

Any recommendations for platforms (free or paid) that helped you when you were starting out? 

Thanks in advance

In your machine learning projects, once a model is deployed, how often do you revisit and adjust the feature engineering process to address issues caused by data drift?
What indicators or monitoring strategies help you decide when updates are needed?