From Academia to AI - the pivot.

Many academics are finding new career paths in applying theoretical expertise to the exploding field of AI and machine learning.

So we sat down with Mark Sebestyen, a mathematician turned ATB data scientist to ask about what short advice he has for scientists who want to make the transition to the applied world of ML.

For researchers with advanced degrees in mathematics, physics, statistics, and computer engineering, moving from theoretical and research applications to the world outside academia opens a world of beauty and passion as theoretical knowledge finds valuable applications in the real world.

There is huge value in having AI and machine learning teams made up of members from enormously diverse backgrounds. It is one thing to know the algorithm or to apply a black box solution, but you need the team to have people who really understand how the algorithm works, and people who have deep user understanding. People who are capable of abstraction, who know the business areas intricately, who know the customer really well, who can see the whole picture, and those who understand the small individual pieces are all essential for success.

Similarly, experienced software developers bring to the table rigorous methodology in model management, documentation, quality testing, and sound development practices.

Successful AI and machine learning teams need a mix of people from diverse backgrounds with theoretical knowledge, research skills, data science mastery, and strong development expertise to piece together the puzzle of applied AI and machine learning.

Luckily, as Edmonton and Alberta move towards being a global centre for AI, there is so much support, funding, and interest flocking to Alberta now. We are really strong in this domain already and it is only going to grow as great minds flock to the province.

For academics and those studying advanced degrees in technical areas or for those who have made the career transition to apply their expertise to AI and machine learning already here are a few pieces of advice:

  1. Be informed about the world at large - it gives you ideas of how to apply novel ideas in your context. Go beyond theoretical applications to make the connection with the real world.

  2. Find a sub-domain in your area that you want to become the best at and keep your focus narrow. Don’t try to be an expert in all things - know your limitations and have deep understanding in your speciality, awareness of what you don’t know, and an open mind to how your expertise can apply to other topics.  

  3. Keep the curiosity going, get comfortable being uncomfortable and exploring outside your expertise area.

Here at ATB and across the AI and machine learning ecosystem in Alberta, we need to be bold and own the knowledge and expertise that we have and to own the solutions to demonstrate to the world the good that can be done with AI and machine learning. We are looking to bring together experts from across fields to create novel solutions and leverage the power of AI for the good of Albertans and the world. Join our efforts at

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