Roles
Unpopular Opinion - Data Scientists Should be More End-to-End
What’s in a name?: The semantics of Science at Lyft
- Lyft outlines the problems in data title ambiguity and proliferation.
The Analytics Engineering Guide
The three kinds of data scientists
One Data Science Job Doesn’t Fit All
Teams
Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department
Building a data team at a mid-stage startup: a short story
Data as a Product vs. Data as a Service
Run Your Data Team Like A Product Team
DataOps Principles: How Startups Do Data The Right Way
Building The Analytics Team At Wish
The four priorities of a one-person analytics team: lessons from Lola.com
Lessons learned managing the GitLab Data team
The Problem With Hands-Off Analytics
To put it another way, the only things self-serve helps scale is SQL…
Analytics is a mess: You can’t stop it, and you shouldn’t try to contain it.
Super Specific Feedback: How to give actionable feedback on work output
Please don’t say just hello in chat
The XY problem is asking about your attempted solution rather than your actual problem. This leads to enormous amounts of wasted time and energy, both on the part of people asking for help, and on the part of those providing help.
Prioritization
One thing that always bugs me about (many) prioritization conversations is that teams often leave out the expected value curve of the work.
Shape Up: Stop Running in Circles and Ship Work that Matters
Prioritizing Data Science Work
Answering one question with data often leads to new questions, so fulfilling requests often creates additional work rather than lowering the amount of work left to do.
The Tool that Will Help You Choose Better Product Ideas
Organizations
Let’s say you were employed as a CTO behind the front lines and you wanted to destroy productivity for as long as you can without getting caught. You can of course make a series of obviously bad decisions, but you’d get fired quickly. The real goal here is to sap the company of its productivity slowly, while maintaining a façade of plausibility and normalcy. What are some things you can do?
Most Data Work Seems Fundamentally Worthless
Mo’ Titles, Mo’ Problems
Coordination Headwind: How Organizations Are Like Slime Molds
An approachable presentation, done in an emoji style, showing why even when everyone is competent and collaborative, you can still get hurricane-force headwinds.
Models for integrating data science teams within organizations
Ten red flags signaling your analytics program will fail
Responses to Negative Data: Four Senior Leadership Archetypes
If you are in the data business – my bread, butter and tofu – you often carry the burden of being the bearer of bad news.
Lessons from Keith Rabois Essay 3: How to be an Effective Executive
In short, the output of your organization is dependent on the number of people that can own projects and see them through to the end.
FAQs from Coaching Technical Leadership
Think Your Company Needs a Data Scientist? You’re Probably Wrong.
The Startup Founder’s Guide to Analytics