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I have been trying to understand this for a long time, but this information proves to be incredibly elusive online.

What are possible jobs that a pure Data Scientist, without much background knowledge, could be hired for in an Engineering team? I am aware, for instance, that supply chain can get some involvement.

I don't mean the Business Intelligence positions, I want to get more involved with the engineering team, working on the products themselves (specially Aerospace or Railway). By "engineering" I mean working in the design phase of the product itself, rather than with post-market features (such as maintenance prediction).

Can a Data Scientist be useful in engineering, even without much domain knowledge?

Is there anyone familiar with this world that could provide some insight? Thank you

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That really depend of the area and the needs of your company a data scientist can fit on everything that produce data ( with a good data collection instruments of course). You are talking about of data scientist in the productions of aerospace or railways industry?

Do you hear about engineering statistics? This is a broad area and there are tens of books about that. I know about engineering statistics area is being used for chemical engineering, mechanical engineering ( thermodynamic statistics or check wiki ), nuclear engineering and civil engineering but there are various applications in more engineering fields.

For example for a beginner in statistics the last chapter of Schaum's Outline of Statistics, 6th Edition Take Chapter 18 Statistical Process Control and Process Capability This method is used for quality control or best said by the book:

18.1 GENERAL DISCUSSION OF CONTROL CHARTS

Variation in any process is due to common causes or special causes. The natural variation that exists in materials, machinery, and people gives rise to common causes of variation. In industrial settings, special causes, also known as assignable causes, are due to excessive tool wear, a new operator, a change of materials, a new supplier, etc.

This chapter focus in charts so that skills on matplotlib, ggplot2, d3.js will come to light!:

  1. GENERAL DISCUSSION OF CONTROL CHARTS
  2. VARIABLES AND ATTRIBUTES CONTROL CHARTS
  3. X-BAR AND R CHARTS
  4. TESTS FOR SPECIAL CAUSES
  5. PROCESS CAPABILITY
  6. P- AND NP-CHARTS
  7. OTHER CONTROL CHARTS

a chart of the book as example:

graphic exampel

If you are a Data Scientist with a solid background of statistics models in the engineering area could help you a lot with Aerospace Or Railway Production Industry.

Bibliography:

Applied Statistics for Civil and Environmental Engineers N. T. Kottegoda, R. Rosso

Statistics for Chemical and Process Engineers A Modern Approach Authors: Shardt, Yuri A.W.

Statistical Thermodynamics: An Engineering Approach 1st Edition, John W. Daily

Statistics for nuclear engineers and scientists.

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