Goto

Collaborating Authors

Winning at interview and preparing for AI-infused recruitment - Business Graduate Association

#artificialintelligence

If your CV was good enough to get you an interview, that's great, but looking good on paper is just the starting point. At interview, you have to demonstrate that you have the skills to do the job and will be a good fit with the team. An interview is an audition – your opportunity to shine and prove you are the perfect person for the role. The actor Harlan Hogan is famous for delivering the catchphrase, 'you never get a second chance to make a first impression…' and it certainly pays to be well prepared. The interview is not, however, just an exercise in self-promotion.


What Video, Artificial Intelligence And Automation Mean For The Future Of Recruiting

#artificialintelligence

It is safe to say that technology is evolving and expanding at an exponential rate. This is what many are calling "digital Darwinism," a time where technology and society are changing faster than most businesses can naturally adapt. A 2018 report found that organizations that are highly invested in digital transformation are more profitable and possess higher market valuations than those that do not. As the founder and CEO of a platform for complete candidate skills and job fit assessment launched in 2003, I have witnessed the rapid evolution of the pre-hire process thanks to technology. It is no surprise that recruiters need to adapt quickly to the ever-evolving environment around them in order to succeed in today's tidal wave of digital progress.


Your Next Job Interview May Be With a Robot--Whether You Realize It Or Not

#artificialintelligence

Many companies have turned to artificial intelligence to lead hiring processes and cherry-pick job applicants… Welcome to the'Wild West of Hiring.' Pixabay When Emily applied for her dream job, she expected to ace her interview. She knew the company inside and out and had prepared to explain why she would be perfect for the role. When she received an invitation to a video platform that would record her responses to a series of questions, she was slightly thrown--she was applying for a people-facing role and had hoped to be able to build rapport with the hiring manager. Still, she hoped that she could still show her personality, even in pre-recorded clips. What Emily didn't realize until after the interview was that the third-party company that hosted the video software used facial analysis technology to screen candidates.


Lyft Designs the Machine Learning Software Engineering Interview

#artificialintelligence

Lyft's mission is to improve people's lives with the world's best transportation and it'll be a slow slog to get there with dispatchers manually matching riders with drivers. We need automated decision making, and we need to scale it in a way that optimizes both the user experience and the market efficiency. Complementing our Science roles, an engineer with a knack for practical machine learning and an eye for business impact can help independently build and productionize models that power product experiences that make for an enjoyable commute. A year and a half ago when we began scouting for this type of machine learning-savvy engineer --something we now call the machine learning Software Engineer (ML SWE) -- it wasn't something we knew much about. We looked at other companies' equivalent roles but they weren't exactly contextualized to Lyft's business setting. This need motivated an entirely new role that we set up and started hiring for. Most companies are open about the expectations for the role being interviewed for, the interview process, and preparation tips.


How Lyft designs the Machine Learning Software Engineering interview - WebSystemer.no

#artificialintelligence

Lyft's mission is to improve people's lives with the world's best transportation and it'll be a slow slog to get there with dispatchers manually matching riders with drivers. We need automated decision making, and we need to scale it in a way that optimizes both the user experience and the market efficiency. Complementing our Science roles, an engineer with a knack for practical machine learning and an eye for business impact can help independently build and productionize models that power product experiences that make for an enjoyable commute. A year and a half ago when we began scouting for this type of machine learning-savvy engineer --something we now call the machine learning Software Engineer (ML SWE) -- it wasn't something we knew much about. We looked at other companies' equivalent roles but they weren't exactly contextualized to Lyft's business setting. This need motivated an entirely new role that we set up and started hiring for.