Roles

Beware the data science pin factory: The power of the full-stack data science generalist and the perils of division of labor through function

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 Engineer

The Analytics Engineering Guide

The three kinds of data scientists

One Data Science Job Doesn’t Fit All

Exec 101 - First 30 days

Teams

Build a Team that Ships

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

Eliminating Toil

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.

Being Glue

Super Specific Feedback: How to give actionable feedback on work output

Please don’t say just hello in chat

The XY problem

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

Effort vs. Value Curves

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

Simple sabotage for software

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?

Don’t Fuck Up the Culture

Most Data Work Seems Fundamentally Worthless

Titles

Mo’ Titles, Mo’ Problems

Amp It Up!

Netflix Culture

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.

Curiosity-Driven Data Science

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

Barrels and Ammunition

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

Taming Slack

How startups die from their addiction to paid marketing

You Don’t Know Your Customer Acquisition Cost