The highest standard in AI — AI Guild #datacareer
Machine Learning Engineer (m/f/d).
An aerospace competency profile.
If your career is in an industry dominated by large corporations, how do you handle a step change to a data career?
He is a Machine Learning engineer.
He has extensive 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 in end-to-end delivery.
For companies and practitioners: The competency profile validates your technical competency and domain expertise in data. It recognizes you as a specialist and advances your career to Senior, Lead, and Director. By practitioners, for practitioners — this service is provided by the AI Guild.
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 more recent, with
- Deep Learning surrogate models that accelerated model building by a factor 10; and
- Emerging MLOps competencies.
The profile balances prior business and industry experience with 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).
Highlight the expertise
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.
Sideways and up: corporate career
I hope you see that “5+ years business innovation and technical leadership” clearly outlines the track record 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.
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The AI Guild is Europe’s leading practitioner community in Data Analytics, Data Engineering, Data Science, Machine Learning, Deep Learning, NLP, Computer Vision, and MLOps.
