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?

Once an ML system moves from a controlled development environment to real-world traffic, the very first cracks tend to appear not in the model, but in the data pipelines that feed it. Offline, everything is consistent schemas are fixed, values are well-behaved, timestamps line up, and missing data is handled properly. The moment the model is deployed, it becomes completely dependent on a chain of upstream systems that were never optimized for ML stability.

Machine learning models are usually trained and validated in controlled environments where the data is clean, well-structured, and stable. Once deployed, the model becomes dependent on live data pipelines that were not designed with ML consistency in mind. Data can arrive with missing fields, schema changes, delayed timestamps, or unexpected values. At the same time, real users behave differently than historical users, causing gradual shifts in feature distributions. These changes don’t immediately break the system, but they slowly push the model outside the conditions it was trained for.

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?

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 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?