Hey everyone,

I’m working on a Power BI report and could use some help figuring out the best way to visualize order details.

Note: The real use case I’m working on is a bit more complex and domain-specific (finance related), but I’ve simplified it here using an order/orderline example that structurally behaves the same.

Order dimension → contains OrderID, date, customer info, etc.

• OrderLine dimension → contains OrderLineID, OrderID (1 order → many lines)

• A fact table → contains OrderLineID and columns like:

  • Amount

  • Discount

  • Tax

  • etc.

All relationships are properly set up, and granularity is at the OrderLine level.

When I select an Order, I want to show a matrix where:

• Each OrderLine is displayed as a column

• Each attribute (like Amount, Discount, Tax) is listed as a row

• The intersecting cells show the actual values

See the example attached.

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?