Category: Data

At Data Sciences, when we interview, we want to get to know the real you, who you genuinely are with all your quirks and talents. Our interview process can be long but we don’t want to make it hard, which is why we’ve built this guide to help you know what to expect and how to prepare – so that you can put your best foot forward!  Interviews at Data Sciences typically span 30 days from application to signed offer but could be longer for leadership roles. This guide is focused on individual contributor roles. While we've highlighted the usual interview formats, it's worth noting that the process may shift based on the specific role, time of year, and team you're applying to join.

It started with Sean and Tom meeting outside some event for the Liberal party of Canada; a grad student in genetics, and a serial entrepreneur with a Masters in Political Philosophy.  “You know, we’re collecting a decent amount of data here, I bet we can optimize this stuff,” said Sean. “Funny you should say that,” said Tom, “I was thinking the same thing, and I happen to know the riding president.” Both, thinking: “I don’t know why, but I have a good feeling about this”...

As a data scientist, I’m often collaborating with a team of digital marketers at DS, notably on designing custom audiences, automating the deployment of ads, and analyzing digital marketing performance. This collaboration is what prompted my idea to send my parents and sister targeted ads on Meta hoping to get their RSVP to my dog's birthday.

February 11th marked the International Day for Women and Girls in STEM, and we're thrilled to showcase some remarkable women in STEM at Data Sciences. We sat down with Jenn, Tayrine, Fatima, and Yujie to delve into their experiences in the world of science. Jenn holds a Ph.D. in Human Genetics from McGill University. She is our Vice President of Data and Analytics at Data Sciences. Tayrine holds a Ph.D. in Information and Knowledge Society from the Universitat Oberta de Catalunya in Spain. She is a Research Analyst on the Insights team at Data Sciences. Fatima Smaili holds a Ph.D. in Computer Science from KAUST (King Abdullah University of Science and Technology in Thuwal, Saudi Arabia) and is a Data Scientist at DS. Yujie, a Data Scientist at DS holds a Ph.D. in Biochemical Engineering from Western University.

Have you ever found yourself in a tricky situation where friends or family ask you about your job as a data engineer, and it feels like you're speaking an entirely different language? The world of data engineering is rich, complex, and incredibly technical, making it challenging to tell what we do in a way that non-technical individuals can easily grasp. But fret not! I've been there too, faced with puzzled looks and perplexed expressions when trying to explain my role. That's why I've come up with something special, an analogy that transforms the often mysterious world of data engineering into something far more digestible for the non-techies in our lives.

In the previous blog, I discussed the impact of Machine Learning (ML) in our current world. We ended with two questions: 1. In this brave new world of AI and machine learning (ML), how do we operate? 2. As individuals, groups, businesses, and governments, how do we navigate a landscape filled with ML? We need to understand what we’re working with before we can plan how to move forward. This blog will dive into some of the machinery that makes AI and machine learning work.

For the purposes of this blog, we’re going to be focusing in on machine learning (ML), a subset of AI that focuses on understanding and building methods that let machines “learn”: training data is leveraged by algorithms to improve the performance of models. These models can describe anything that you are trying to predict or classify based on underlying characteristics.