I first learned about Plum through the Career and Co-Op Centre at Wilfrid Laurier University, where I’m currently in my third year of studies for my Bachelor of Business Administration. I’ve always wanted to try working at a tech start-up, so when I discovered the Marketing Coordinator role at Plum, I knew it would be the perfect fit for my second co-op term.
Earlier this week, our CEO Caitlin MacGregor joined Ben Eubanks on the HR Tech Talks livestream to talk about how Plum is using psychometric assessments at scale to reimagine talent management.
There was a popular Tweet flying around the internet last summer that summed up the past year perfectly. It said, “Don't know about y’all but I could really go for some precedented times.”
There are countless resources and technologies available to help organizations become more agile, efficient, and provide a competitive edge. But HR leaders know there is one resource that is infinitely valuable yet often overlooked: employees.
Effectively attracting, harnessing, and deploying the talent within your company is always essential, but it’s especially crucial in times of uncertainty. At Plum, we know you’re concerned about how to manage your talent needs, from hiring and workforce planning, to saving money. That’s why this latest blog post is designed to help you move your talent programs forward during the unprecedented time we’re all living in right now.
If your company has moved remote in response to the COVID-19 physical distancing measures, you’re likely set up in your new home working environment by now, and figuring out what’s working well for you. But you're also probably discovering (with possible alarm) what isn't. This new reality so many of us are finding ourselves in comes with many perks, but also many challenges, and we’re here to help.
Plum's CEO Caitlin MacGregor originally published this blog to SAP's Digitalist Online Magazine.
In the future of work, talent management teams will need to get their hands on predictive data to make the best talent decisions.
Last year, information leaked that Amazon tried to build an algorithmic system to analyze resumes and suggest the best hires. It failed. Hard.