Machine Learning Engineer (m/f/d). Competency profile No 2.
Updated: May 9, 2021
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 the example of a Machine Learning engineer.
He has extensive experience industrial experience in aerospace (France, Europe) and has been a team leader before. 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 strikes a balance between 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).
The competency profile was built as part of the AI Guild career development program.
Focus: What is in and what has been left out?
Perhaps you noticed that the two central pillars highlight the technical expertise in depth. For each flanking competency (i.e., MLOps, Data Analytics), a summary statement is provided.
On a typical CV, the data analytics track record 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?
Here is what I hope you see: "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.
How to get your competency profile?
Are you in Year 2, 3, or 4 of your #datacareer? Would you like to find out how you efficiently show and develop your expertise? It takes 60 days to complete the competency profile. All you need to do to start is point us to your LinkedIn or GitHub profile (or similar) here.