If you’re like me, mental well-being has been a challenge to maintain during the pandemic. I find it especially difficult to get ahead of work between back-to-back calls, long slack threads, and a never-ending work backlog.
I believe this is partly because of a work culture unprepared for remote work, as well as the fact that productivity tools are designed to maximize speed and quantity of output, not our mental health. This leaves the door open for startups to re-invent productivity tools and help companies retain workers in a remote-first work environment.
A 2020–2021 study by Microsoft showed that 41%…
If you already come out of debates laughing and high-fiving everyone, then you can stop reading. I’ll just be wasting your time.
My story is only of value to folks that, like me, see clear winners and losers during debates, have a tendency to be aggressive, and often come out feeling exasperated.
Spoiler alert: I now preach a different way of debating, and nobody has to get hurt. It’s an approach that brings people together to solve a problem, not compete against one another. It’s rooted in collaboration.
I believe that ideas can’t improve if they go unchallenged. How do…
This is not a blog post on improving your job search process (read this instead).
This is a blog post on what we (my colleagues Joshua Loong and Lewis Davies and I, as hiring managers for advanced analytics at Best Buy Canada) are looking for in an applied data scientist. If this helps you land a job, great. A more fulfilling career, amazing. But full transparency: I’m writing this with the hope of improving the quality of candidates in our recruiting pipeline.
“Looking for a data scientist that’s a solid average.”
Have you ever heard someone say that? No.
It’s been shared that 87% of data science projects don’t make it into production. The problem is well documented (see here, here and here).
In my experience building analytics products at Best Buy Canada, applied data science projects rarely fail because of the science. They fail because the model couldn’t be integrated into existing systems and business operations. We have had sound models showing good accuracy, even with proofs of concept demonstrating value, yet still fail to get deployed into production. The challenge isn’t POCs, its scaling.
Specifically, a gap between data scientists developing the model and engineers implementing it…
I write to ask why at night: challenge assumptions, explore blind spots. I build analytics products in the day.