• Chris Armbruster

Machine Learning Engineer (m/f/d). Competency profile No 2.

Updated: Aug 28

The competency profile validates your domain expertise in data. It recognizes you as a Senior practitioner or advances your career to the Senior level.

By practitioners, for practitioners - this service is provided by AI Guild members recognized for their expertise.

 

Here is an example of a Machine Learning engineer.

He has extensive experience industrial experience in aerospace (France, Europe) and has been a team leader. Switching from a business analytics role to Machine Learning, he utilizes his significant industry competence to propel himself to lead on end-to-end delivery.

 

What do you see in the competency profile?

This profile shows an industry track record with

  • Expertise in time-series analysis with model optimization for a particular problem: aircraft noise and maintenance; and

  • A broader background in utilizing data analytics to drive innovation.

His shift to Machine Learning is indicated as more recent, with

  • Deep Learning surrogate models that accelerated model building by the factor 10; and

  • Emerging MLOps competencies.

The profile balances the prior business and industry experience and the upgraded technical competence.

You are looking at the profile of an emergent ML leader for deployment in the aerospace industry. What is leadership? Your ability to integrate ML competence, experience with industry data, and business sense (e.g., cost-saving).

 

Focus: What is in and what has been left out?

Perhaps the two central pillars highlight the technical expertise in depth. A summary statement is provided for each flanking competency (i.e., MLOps, Data Analytics).

The data analytics track record on a typical CV would command much space as 'professional experience.' By providing focus, the competency profile makes it possible to consider where you are coming from and where you are headed and strike a balance that moves your career forward in the desired direction.

 

Where is his #datacareer headed?

I hope you see that "5+ years business innovation and technical leadership" makes the track record clear and indicates the motivation to enlarge the scope from time-series analysis to Machine Learning more broadly. Also, the move to ML includes working with data from the same industry. I think that data domain expertise matters increasingly.

The excellent move is to 're-use' your prior experience and let it advance your ML career.

 

Do you want to progress to Senior, Lead, and Director?

It takes 60 days to build your competency profile. You can find out more by booking the first conversation to gain more insights at https://www.datacareer.eu.

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