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?

We often hear about ML models achieving amazing accuracy in research papers or demos. But in the real world, things aren’t so simple. Data can be messy, incomplete, or biased.

Features that seem obvious may not capture the underlying patterns. Sometimes even small errors in labeling can completely change model outcomes.

How did you approach them, and what lessons did you learn? Sharing your experiences can help the community avoid common pitfalls and discover better strategies for practical machine learning.

In real-world data environments, perfection is rare. Sometimes a model with 88% accuracy performs better in production than one that hits 95% in the lab.
Would love to hear your approach , what metrics or signals tell you it’s time to deploy? And how do you balance performance with practicality in your ML workflows?

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?

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. 

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.