Predictive Hire was founded four years ago by two friends from England who have known each other since they were kids.
They were both recruiters who came to realise that the recruitment process was unfair and inefficient. They found themselves making calls that affected people’s careers without having any concrete way of knowing if they were right for the job. They thought there had to be a better way, so they spent some time with a team of data scientists and realised that organisations could bring Artificial Intelligence (AI) to the task of identifying a great recruiting profile, and use that as a benchmark against which to test everyone who applied to the organisation. In doing so they sought to create fairness and help more people find the right job.
Fairness and opportunity mean a lot me.
My family emigrated from Zimbabwe when I was 10 years old, the third of four children. My Dad gave up a comfortable life with no financial worries because my Mum wanted us to move to Perth. We went from having a quarter acre block and an amazing life (at least it seemed amazing through the eyes of a naive 10-year-old) to living in a tiny apartment.
We help companies find better people faster. That means making sure they’re hiring for what ‘great’ looks like, based on data not on human bias, while also giving the applicants a valuable experience.
After University I was again lucky to join the Boston Consulting Group (BCG) where I just managed to get through the door. That experience gave me more connections and, as a result, further opportunities opened up at REA Group. Through connections, hard work, and my own heritage, I was afforded opportunities that may have eluded many others -but I wondered what it was like for those less fortunate to navigate the world, to find success and be given career opportunities.
Even the great companies I worked for have their biases. Someone from a private school was presumed to have a natural inclination for management consulting while a software engineer from a startup was automatically seen as a better fit at a tech company than a programmer who’d worked in a bank. Biases were self-reinforcing and locked many people out.
My mission is to try and fix that, and to ensure that everybody gets the same chances in a way that is fair across the board, agnostic of age, gender, or ethnicity.
We do that by having applicants answer questions about values and behaviours using conversational AI. The questions are adaptable and dynamic, which is where machine learning comes in – the machine either satisfies itself with the answer or moves to a deeper set of questions on the same topic. It mimics what humans try to do but don’t do very well.
The result is that everyone is given an even opportunity; fairly, equitably, and efficiently versus just a few privileged people getting in the door.
Right now we’re focused on recruitment. The next stage is promotion, which I think is even less calibrated than recruitment. Then it’s combining the right people to create high-performing teams.
Equally we’re in constant communication with our corporate customers, taking on their ideas with the attitude of: can we do that, let’s work on it, let’s release it. We can move from thought to execution within a day, and I love that. When I was a lawyer I felt removed from reality because it glorified execution, shuffling paper here and there. As a management consultant I felt a little closer to the point of impact, but still it was mainly writing PowerPoint decks and leading workshops.
So there’s something invigorating about sitting with the guy who’s looking at the screen, analysing the data and adjusting the models. I couldn’t get closer to the product unless I became a data scientist myself. It just feels real. It feels like I’m closer to doing something that’s really affecting people and impacting their lives, providing a path to opportunity for people who might not otherwise be considered.
Right now we’re focused on recruitment. The next stage is promotion, which I think is even less calibrated than recruitment. Then it’s combining the right people to create high-performing teams. We’re becoming the engine that connects the dots between people and performance.
I feel lucky to be in a space that’s a bit unchartered, imagining what our product could do in the future and figuring out how to implement fast for the here and now. I’m not a founder, but I behave like a founder, and I believe that everyone in an early stage company has to feel that way. That means we own it, we care about it, we help each other out and we’re all in.