December 2022: Data Science Career Resources
Books and blogs to get you thinking about your career in data science
Hello, fellow datanistas!
First off, congratulations to both Team Argentina and Team France for making it to the World Cup Final! h/t to the GOAT Lionel Messi for his incredible performance; this was a fairytale ending to his professional footballing career.
This edition is the last newsletter of the year; how time flies! At the same time, our family welcomed our 2nd child last week, hence the delay here. In this edition, we will be exploring books and resources on the topic of data science careers.
Data Science Careers
As a new manager, I've been thinking a lot about how to build a team. That involves hiring people, training them, motivating them, and guiding their career development. There is one book in particular that I would like to recommend: The Care and Feeding of Data Scientists by Michaelangelo D'Agostino and Katie Malone. The book provides a great framework for a wide range of topics: hiring, career progression, motivation, how to work, and more. I've used it as a reference guide over my first 1.5 years as a data science team lead, and I think it will be valuable as a resource for other leaders. You can find it online here.
Staff Engineers and Data Science Fellows
Another great book I recommend is "The Staff Engineer's Path". As I was reading through it, I saw incredible parallels between the data science world at Moderna and the software engineering world at large. For example, the book describes why staff engineers (or, analogously, data science fellows) are needed within an organization. The book also describes the various hats that staff engineers wear, such as mentoring junior engineers, being a technical lead or architect, and sometimes providing technical guidance to senior leadership. I see data scientists who have not traversed the "Director" path in their career playing analogous roles in their domains. I think this book is a great read for anyone interested in progressing in a primarily technical sense rather than in a managerial role. You can find the book here.
Our department head, Dave Johnson, assigned this book as mandatory reading in preparation for our department meeting. The book, written by Kim Scott, is all about how to be a good boss that gets our team to perform at their best without, pardon my foreign language, being an asshole. In my opinion, the book is a must-read for anyone who is a manager. Culture can get in the way of the commonsensical ideas outlined in the book; hence, the book is an important reminder to us about how to be a good boss: to care personally, challenge directly, and be kind. You can find the book here. I also bought the audiobook on Libro.fm, since I prefer to listen to books over reading and because I’m intentionally avoiding enriching already-rich individuals (such as the owners of Audible). If you’re interested in a referral link to sign up, where I think we both get a free audiobook, leave me a comment on Substack, and I’ll paste one where you can sign up.
Should a team specialize or generalize?
StitchFix has an awesome perspective that runs counter to how some teams operate. In particular, the author, Eric Colson, argues that a data science team should be generalists rather than a narrowly-scoped team of functional specialists. Colson correctly identifies hand-offs as a source of inefficiency, and how having your data scientists be generalists who take a project from conception to completion without needing coordination between functions allows for faster iteration times. Colson also identifies the dichotomy between solution-building, a.k.a. execution (usually the domain of Engineering) and the discovery of new business capabilities, a.k.a. exploration (which is the intended domain of Data Science). The post is worth a read, and you can find it here.
Software Engineering for Data Scientists
My experience as a data scientist for the past five years has shown me that we need to think about code quality and software engineering practices if we want our projects to have longevity. After all, building a prototype in a notebook is one thing, but it's a different ball game to build a production system. Hence, when I found the book, Software engineering for data scientists, my eyes lit up. While the book is still being written, I perused the table of contents, and found myself nodding in agreement at the topics being covered: testing, documentation, and refactoring. It looks like an awesome book to pick up! Incidentally, in my Essays collection, I have a section on Software Skills that can serve as an introduction to the topic.
From my collection
I wrote a blog post on hiring, back in 2021. I wanted to share it in case it helps upcoming data scientists figure out what goes on in a hiring manager's head. You can find it here.
I started using the Warp terminal, and it's got some next-level capabilities, such as AI-assisted shell completion, a better terminal command separator, multi-line editing, and the ability to write in-terminal notebooks for documentation. If this piques your interest, I have a referral link for you to join (disclaimer: I'm in the game for a free t-shirt, that's all!)
Finally, I am working on a longer-form reflective essay on my first year as a data science team lead. It will reveal many of the things I've learned over the past year and hopefully be a useful resource for other data science team leads and aspiring data scientists. I hope to get it out within the next few weeks, and I will share it here when it is ready!
That's all for this edition. Please share the newsletter with your friends if you found it useful! Wishing you a Merry Christmas and a Happy New Year! See you all in 2023 :).
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