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.
Data Science: Reality Doesn’t Meet Expectations
Does my Startup Data Team Need a Data Engineer?
When did analytics engineering become a thing? And why?
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
A reference guide for fintech & small-data engineering
- tl;dr: Design thinking, workflows, human relationships, statistical methods, stories
Data Science Foundations: Know your data. Really, really, know it
Low-Context DevOps: A new way of improving DevOps/SRE team culture
12 Signs You’re Working in a Feature Factory
DataOps Principles: How Startups Do Data The Right Way
Most companies suck at disseminating factual knowledge. Yours probably does too.
Strengthening Products and Teams with Technical Design Reviews
Towards an understanding of technical debt
Scientific debt is when a team takes shortcuts in data analysis, experimental practices, and monitoring that could have long-term negative consequences.
Machine Learning: The High Interest Credit Card of Technical Debt
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.
One thing that always bugs me about (many) prioritization conversations is that teams often leave out the expected value curve of the work.
The Ten Fallacies of Data Science
There exists a hidden gap between the more idealized view of the world given to data-science students and recent hires, and the issues they often face getting to grips with real-world data science problems in industry.
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…
Shape Up: Stop Running in Circles and Ship Work that Matters
Four communication techniques for solving technical problems
Analytics is a mess: You can’t stop it, and you shouldn’t try to contain it.
Code Review
How to review an analytics pull request
The Art of Giving and Receiving Code Reviews (Gracefully)
How to Do Code Reviews Like a Human
Unlearning toxic behaviors in a code review culture
Google Engineering Practices Documentation
Organizations
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
The Tool that Will Help You Choose Better Product Ideas
Think Your Company Needs a Data Scientist? You’re Probably Wrong.
The Startup Founder’s Guide to Analytics