Christopher Gandrud

Senior Data Science and User Insights Leader at @spotify
Previously Director of Economics + Experimentation @ZalandoTech

[Matt Blackwell @matt_blackwell]

New short 📜 from me, Nicole Pashley, and @dom_val on THIS ONE WEIRD 90-YEAR OLD TRICK (Neyman allocation) that lets you leverage the pilot study of your experiment to maximize statistical efficiency. https://t.co/v71BGYr13V

[Andrew White 🐦‍⬛ @andrewwhite01]

Forget DALLE, PALM, M1 Pytorch. This is the biggest advancement in computer science this year: LaTeX math in github
https://t.co/Lm7wX3RX5M

[Christopher Gandrud @ChrisGandrud]

@conjugateprior @patrickdoupe @jon_m_rob Thanks for inviting me @conjugateprior. It was fun. I don't think I shared my slides: https://t.co/oVS2aOj8NW

. . .where I shared Fernández-Loría and Provost’s hot take. Probably the most thought provoking thing I’ve read recently.

[Shreya Shankar @sh_reya]

I probably should have written this years ago, but here are some MLOps principles I think every ML platform (codebase, data management platform) should have: 1/n

[Gergely Orosz @GergelyOrosz]

What are things that work?

Focusing on value generated for the business.

Getting feedback from the team on what works & what doesn't.

Eliminating biggest time wasters.

Qualitative feedback.

Investing in ways to create leverage for engineers.

Iterating how the team/org works

[Sean J. Taylor @seanjtaylor]

Nice post by @jfkirn, one of the best PMs I've ever worked with, covering interesting aspects of experimentation at Lyft:
- network effects
- switching mindset from single decision to "always on" experiments
- role of platform in aligning org decisions
https://t.co/o3iivGcCBX

[Chip Huyen @chipro]

New blog post! Discussing:

1. Whether a data scientist should be fullstack
2. What caused the unreasonable expectation that DS should know Kubernetes
3. An overview of the tools that can help abstract away infra to allow DS to own a project end-to-end 🚀

https://t.co/4I3fDPaHe6

[Sean J. Taylor @seanjtaylor]

To some extent, we all harbor this conceit that we alone could do something really amazing. But all the most impressive stuff is accomplished through some monumental coordination of talent and labor. Organizations are the best technology we've invented as humans.

[Christopher Gandrud @ChrisGandrud]

Beta #pythoncode package for batch updating statistics (mean/variance). Useful for privacy preserving A/B tests: https://t.co/COMoFNt9EZ

[Christopher Gandrud @ChrisGandrud]

Notes on developing and distributing #python packages: https://t.co/fuwCs4vYxp

[Christopher Gandrud @ChrisGandrud]

@ZalandoTech is looking for 2 senior backend engineers to develop and improve our high load A/B testing and continuous delivery system.

Also, lots of opportunities to work with advanced data scientists to bring research to production.

Get in touch at: https://t.co/oaJXVljpgZ

[Christopher Gandrud @ChrisGandrud]

We're looking for a leader who loves scaling experimentation, both with a high performance technical platform and through a product management organisation @ZalandoTech

If you're interested, apply here: https://t.co/P2tJruwWQe

[Christopher Gandrud @ChrisGandrud]

We're looking for a leader who loves scaling experimentation, both with a high performance technical platform and through a product management organisation @ZalandoTech

If you're interested, apply here: https://t.co/P2tJruwWQe

[Christopher Gandrud @ChrisGandrud]

@ZalandoTech is looking for 2 senior backend engineers to develop and improve our high load A/B testing and continuous delivery system.

Also, lots of opportunities to work with advanced data scientists to bring research to production.

Get in touch at: https://t.co/oaJXVljpgZ

[Christopher Gandrud @ChrisGandrud]

@ZalandoTech is looking for a leader who loves scaling experimentation, both in a high performance technical platform and through a product management organisation.

If you're interested, apply here: https://t.co/dt2q1y1jqP

[Sean J. Taylor @seanjtaylor]

If I could build it again, I’d start with automating the evaluation of forecasts. It’s silly to build models if you’re not willing to commit to an evaluation procedure. I’d also probably remove most of the automation of the modeling. People should explicitly make these choices.

[Ronny Kohavi @ronnyk]

Slides from my Amazon Alexa lab fishbowl talk at https://t.co/I1mjChGgY7
Short 20 minute intro on #experimentation followed by Q&A on trends, culture, institutional memory, experts, OEC, platform, and mobile.
#abtesting #hippo #experimentguide @kskripashankar

[Edward Kennedy @edwardhkennedy]

This work by @CMU_Stats PhD student @iwaudbysmith is *extremely* cool

Surprisingly, you can do safe, anytime-valid causal inference - even in complex observational studies, where asymptotics usually dominate! Has important implications for modern online data collection & beyond

[Christopher Gandrud @ChrisGandrud]

Does anyone else do this:

I “like” content on twitter and even buy books on Amazon, not because I have a strong interest in that tweet or even reading that book,

but I want to teach the recommendation algorithms that I like this type of stuff and want to see more.

[Sean J. Taylor @seanjtaylor]

If I had an hour to build a model, I’d spend 55 minutes building a fast and unambiguous evaluation procedure and 5 minutes trying out models.