By James Smith | 3 May 2021
The more women working in technology, the better off the IT sector is. It is as simple as that, according to industry experts and insiders appearing on the final session of the ATMC/Employability.life IT Job Pathway Webinar Series.
Joshua Stroup, Software Engineering Manager at NICE inContact and Moderator for the IT Job Pathway Series, says despite its at-times seemingly “male” exterior, women should definitely feel encouraged about pursuing a career in IT.
“The reason I am so embraced in the ‘women in technology’ aspect of the industry is that women think differently to men,” he commented during the AI/Machine Learning webinar.
“Guess where innovation comes from – different thinking, and collaboration with that thinking. If you want a more innovative leadership and a more innovative way of approaching your relevant field, you want different people on board who think in different ways.”
Phani Karnati, Head of Artificial Intelligence and Machine Learning at ViHaVe.ai Innovations, was a guest on the AI/Machine Learning webinar and applauded women’s dedication to the industry despite the outside pressures and time demands many women face on a daily basis.
“Women should be there in the IT industry,” Phani said. “Without their participation, nothing can be achieved. They juggle a lot of responsibilities, such as family. For men, it’s a bit easier – they can push everything aside and just come to work.
“Despite all of these extra challenges, women do some really great work in IT; you can count on the job being done. I really encourage women to consider a career in not only the IT industry, but in leadership roles within the industry as well.”
Also appearing on the AI/Machine Learning webinar was Karl Macek, Principal Consultant of AI and Analytics at DHL IT Services. Karl had some invaluable advice for students thinking about entering the AI/Machine Learning sphere.
“To put it simply, the first thing you need is a good, foundational knowledge of Machine Learning [ML],” he said. “If you don’t have it on your curriculum, perhaps you might look into some online courses. Without some basic systematic knowledge, it would be very difficult to penetrate this area.
“You can’t approach it with an attitude of: ‘I’ll read one tutorial here and one tutorial there.’ You need to know about the changing landscape of ML well.
“The more you know about the landscape, the closer you are to knowing, for example: ‘This program has a simple solution; the formula is in this book, on page 321, it’s in this frame, in the bottom part of the page.’
“Having such knowledge means you’re not spending two-three months trying to solve the problem.”