5 Career Tips from Women Leaders in Machine Learning
Understanding how important representation, role models, and mentoring had been to my own career journey, I started a network to support other Amazon employees looking to pursue a career in machine learning (ML) and artificial intelligence (AI).
Open to anyone working at Amazon, the global Women in ML/AI group hosts regular networking events and organizes panel discussions with industry experts on career development.
To discuss learnings from our professional journey, I sat down with fellow board members, including senior documentation manager Michelle Luna, senior software development manager Anna Khabibullina and general manager and product lead Shubha Pant. Here are some of the advice we found invaluable when launching and building a career in the field.
1. Put Yourself out There and Make Connections
Luna, Khabibullina, Pant and I are all proof that there are many paths into ML and AI — from the traditional and linear, to the more unconventional.
I started out in the technology and media communications sector in Germany, where one of my first roles was in market research. This is where I realized that I wanted to understand the fundamentals of data science and ML. I have a business background, but I just kept building my network with people in the field and pursuing data science roles and internships.
Luna: “I had no real machine learning experience before I joined AWS. I had worked in language translation software 25 years ago, so I was sort of pulling at a thread from a past career, but this experience seemed to get me in the door. I have some DevOps experience too, and this applies to my role now in ML, which I hadn’t even realized.
I would say don’t be afraid of putting yourself out there, no matter what your career path in technology has been. One big thing our members want is a place to network with other women who already work in ML. I can’t emphasize enough the importance of reaching out and building those connections.”