Never Put the Low Priorities First


There’s a Calvin and Hobbes comic that perfectly captures why your data science career isn’t advancing.

In the strip, Hobbes suggests Calvin prioritise his chores over having fun, so that once he’s done, he can enjoy the rest of the day without worry.

The punchline is that by the time Calvin finishes his chores, the day is over and his mother appears to send him off to bed.

Many data scientists are Calvin.

We tell ourselves we’ll work on that high-impact analysis after we finish these status updates.
We’ll have those stakeholder conversations once we finish this admin task.
We’ll build that game-changing solution right after we respond to these “urgent” emails.

Here’s the thing…

As Calvin discovered, chores have a habit of expanding to fill your available time.

And no data scientist has ever been promoted for achieving inbox zero.

Try this instead: Start with what matters.

  • Block the first 2 hours of your day for high-impact work;
  • Have conversations with stakeholders about real business problems;
  • Build solutions that move the business forward before answering your emails;
  • Then, squeeze your admin tasks into the last 30 minutes of the day.

Your career advances based on the problems you solve, not the emails you answer.

Never put the low priorities first.

Talk again soon,

Dr Genevieve Hayes

Data Science Impact Algorithm

Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.

Read more from Data Science Impact Algorithm

When I started my career, data science didn’t exist as a field. I trained as an actuary and statistician and those were the tools I relied on in my earliest roles. Then, around 10 years ago, I started hearing about the wonders of machine learning and became worried that my traditional training was no longer enough. So, despite already having a PhD in Statistics, I went back and completed a Masters in Machine Learning. Then came the AI wave – ChatGPT, large language models, generative AI – and...

The most valuable lessons I’ve learned in my data science career weren’t learned in a classroom. They came from conversations with people who’d already figured things out the hard way. My podcast has been a more valuable learning tool for me than all of my university degrees combined. Over 100 episodes, I’ve had the chance to speak one-on-one with some of the sharpest minds in the industry - CEOs, best-selling authors and leading researchers - on everything from cutting-edge AI to what it...

In 2015, I fell in love with a job I would never have. I’d just attended a conference where people were talking about machine learning and data science as the way of the future. I returned to the office eager to learn more and started down the data science rabbit hole - where I stumbled across an article about the recently established NYC Mayor’s Office for Data Analytics. They were using data science to locate illegal cooking oil dumping in the city’s sewers. To coordinate emergency services...