What I Read #2
Managing a data team, delegating tasks, data team branding, and more
Readers, I hope you’re enjoying your day wherever you are. In this issue, I curated a reading list for data science and machine learning managers. Being a data science manager is not easy. There’s a lot more ambiguity and uncertainty in data science than any other discipline, and while there’s a plethora of resources for software engineering and business managers, it’s difficult to find good resources targeted towards data science managers (after all, data science is still a new field!).
Though I don’t agree with everything that’s said by these authors, I find their thoughts worth considering, especially if you’re a first-time manager. From building and managing a data team and delegating tasks to data team branding, it’s my hope you find these resources useful for your data career whether or not you’re interested in becoming a manager.
In no particular order:
Managing the abstraction mix by Katie Bauer. Data science managers often wear many hats, context switching and working with so many different partners across the org. How should they manage all these?
Delegation is a superpower by Caitlin Hudon. First-time managers often find themselves inundated with both technical and non-technical tasks. Delegate them one at a time, and give your reports a chance! It’s not about you but us.
Building a data team at a mid-stage startup: a short story by Erik Bernhardsson. A funny story I can relate to as the head of data science at a hyper-growth startup. Lots of moving pieces to manage.
Run your data team like a product team by Emilie Schario and Taylor Murphy. Data team is only useful if it can move the business in a meaningful way. Data science as a service model is often stripped of the autonomy it needs and therefore should probably be avoided.
Data team branding by Emily Thompson. Branding is important for data science! One of the many responsibilities of a data team leader is to form strong relationships with business partners across the org early on, and shifting org culture is easier said than done.
What I’ve learned from my best managers by Deb Liu. I’ve had a fair share of good and bad managers throughout my data career, and when I get a chance, I’ll probably draft my own experience in future posts.
And if you missed what I wrote on management:
Free-flowing project management for data science — I tell the story from my personal experience moving the team from Scrum to Kanban and ultimately settling on the free-flowing approach to manage data science projects and teams.
Effective software engineering — How can the engineering teams ship and iterate faster? Bring them closer to the business by getting them involved in not only the execution but the planning in the early stage.
2022 in review:
As we near the end of 2022, I expect life to get busier cleaning up loose ends and planning for the year ahead. When I started this newsletter, my goal was to write once a month, and overall, I’m proud of achieving it. I hope to write more next year, time permitting, so stay tuned.
Here’s a tentative list of topics I have in mind to write about in 2023:
Building and scaling a data team (this is a big one)
Managing data science and machine learning teams
Tips and how-tos for surviving work, a data science edition?
Dealing with jerks at work, how to bullshit through meetings… you name it!
Growth hacking strategies for (DTC) startups
My own data science journey
And more!
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As always, if you have questions, leave a comment or send me a note on LinkedIn. Wish everyone has a safe and happy holiday. Thanks for reading!
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About this newsletter
I’m Seong Hyun, and I’m a data scientist, machine learning engineer, and software engineer. Casual Inference is a newsletter about data science, machine learning, tech startups, data teams and careers, with occasional excursions into other fun topics that come to my mind. All opinions are my own.