Excellent Resources

Career / Tech

https://blog.xaviershay.com/articles/avoiding-project-cancellation.html
Xavier Shay’s has an excellent blog I review often – this is just a recent post I found helpful to understand high level decision-making and investment.
If your project is long running with back-loaded benefits (“pay off a year down the line”): you are a high risk project.

https://dev.to/baweaver/beyond-senior-progressive-influence-254g
Another great blog by a former colleague Brandon Weaver about career progression in tech at the Staff level and higher.
The big point of all of this is that influence is very much about using only as much power as is necessary to solve the problem at hand.

https://medium.com/@saumil/avoid-the-reorg-from-hell-with-six-key-principles-f8c9cbdfb0bd
Great overview of re-orgs and communication from Saumil Mehta, a GM at Square. Their article on compensation is also super insightful.
First off — any reorg will usually feature a very small group that is responsible for the actual design. A somewhat larger group may be consulted. A senior leader may be the final approver. And a much larger group would be usually informed. It is usually critical to communicate with each of these groups in order, bringing them in the know (ITK) at the right time.

https://lethain.com/promo-pathologies/
Great article on promotions and reasons for rejections.
When working within a team, particularly a team staffed with more senior or a number of similarly senior individuals on the same problem, it’s hard to attribute impact across that team.

https://staffeng.com/guides/present-to-executives/
Great tips on presenting to executives.
If you show up as resistant to feedback, then they’ll start swallowing their comments, and you’ll get relatively little out of the meeting.

https://medium.com/backchannel/how-the-tech-press-forces-a-narrative-on-companies-it-covers-5f89fdb7793e
Cycle of tech company narratives by Aaron Zamost.
You’re never as good as everyone tells you when you win, and you’re never as bad as they say when you lose. — Lou Holtz

https://blog.xaviershay.com/articles/short-mantras-to-interrupt-imposter-syndrome.html
Great for anxious folks to gain some perspective.
If I’m not doing a good job, someone will tell me

https://paulgraham.com/lesson.html
Great to come back to every now and then and a perspective on formal education.
But wasting your time is not the worst thing the educational system does to you. The worst thing it does is to train you that the way to win is by hacking bad tests.

Data Science

https://hbr.org/2018/12/what-great-data-analysts-do-and-why-every-organization-needs-them
Excellent overview of the tradeoff between different DS specialties.
Excellence in statistics: rigor…. Excellence in machine learning: performance…. Excellence in analytics: speed

https://towardsdatascience.com/the-analytics-hierarchy-of-needs-6d57d0e205e2
Excellent Article by a colleague Ryan Foley on the progression of data tasks available to do. He also wrote an excellent article on a ‘money tree.’
The general idea of the analytics hierarchy of needs is that you should not move up the hierarchy until you’ve done the basics in the prior step (i.e. no deep analysis before metrics are defined & tracked, no dashboards built before you’ve started collecting & cleaning your data, etc).

https://developer.squareup.com/blog/product-analytics-at-square/
Great overview of Product Analytics (now Product Data Science) at Square by Fan Zhang (colleague)
With Square growing and its products expanding, developing, and launching, an analytics support gap emerged for our SaaS product teams. In their infancy, these new product teams needed to learn, iterate, and grow their reach. The SaaS products were not at the same scale as our payments business, which leveraged existing data infrastructure to automate decisions. Instead, product teams needed low-friction data access to make quick decisions. More importantly, they needed insightful analysis of seller behavior to drive product and marketing strategy. Addressing this gap was the catalyst for growing our Product Analytics discipline.

https://review.firstround.com/the-startup-founders-guide-to-hiring-a-data-scientist
Great article on companies hiring their first data scientist by Mengying Li. They also have a nice interview with the first data scientist at Notion
To gauge whether it’s the right time to bring your first data scientist onto your team, there are a few key points for reflection: First, has your company reached a point where you have enough data to generate quality insights? And second, do you have the right tools and support to realize the ROI from a data scientist’s insights?

https://medium.com/@selwyth/will-the-real-data-scientist-please-stand-up-1e6395d467ca
Great reminder to be authentic as a data scientist, written by a former colleague David Feng. Another great blog post by David on setting subscription prices.
Trusting the wrong data scientist can result in no-delivery at best, decisions made on incorrect assumptions with wrong interpretations at worst.

https://ianwhitestone.work/good-ds-bad-ds/
Great version of the Good/Bad PM article by Ben Horowitz.
Good DS is obsessed with solving business problems. They relentlessly search for them, and then bring out the right tool once found. Bad DS is obsessed with applying a specific technology or tool. They’ll orient their search for problems around the tool they are looking to use.

https://blog.pragmaticengineer.com/what-is-data-engineering/
Great primer of distinguishing some different data terms and high level of the infrastructure.
In short, data engineers play an important role in creating core data infrastructure that allows for analysts and end-users to interact with data which is often locked up in operations systems.

https://medium.com/@gokulrajaram/the-second-most-important-metric-for-every-company-df958ff8c5ec
Great article by the legendary Gokul Rajaram (former exec I worked with at Square) – his linkedin, twitter, etc posts are usually very solid. Another great article on his SPADE framework for decision-making.
The second most important metric for every company is a check metric on the NSM. It’s a metric that constrains the NSM and ensures that the NSM grows in a way that is sustainable and creates long-term value.

https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
Solid overview of production ML guidelines, granted moreso for MLEs, but still has some great gems.
Rule #3: Choose machine learning over a complex heuristic

https://counterfactuals.co/case-studies
Nice new blog by a former colleague Tyler Hanson about end to end DS effectiveness.
The perceived bottleneck for these teams is not “insights”; it’s the manipulation and preparation of data. This is where the de-facto scope of a data team becomes clear: data analysts are on a separate team and defined by their technical skillsets in manipulating and accessing data, not their analytical prowess. When analysts partner with other teams, they are called upon to solve “data-shaped problems” rather than actually conduct data analysis, because the former is what actually separates them from the rest of the business.

https://www.kdnuggets.com/2021/03/ultimate-guide-acing-coding-interviews-data-scientists.html
Excellent guide to data science coding interviews by my Square colleague Rob Wang! Another good followup article on product case interviews.
Lots of real-world data science projects are highly collaborative, involving multiple stakeholders. Data scientists who are equipped with stronger fundamental CS skills will find it easier to work closely with engineers and other partners.

https://www.abartholomew.com/writing/your-first-data-hire
Great tips on hiring a first DS / what to expect there.
You’re looking for someone whose responsibilities will straddle two realms: Analysis and Analytics Engineering.

https://huyenchip.com/machine-learning-systems-design/design-a-machine-learning-system.html
Excellent overview of ML end to end
Designing a machine learning system is an iterative process. There are generally four main components of the process: project setup, data pipeline, modeling (selecting, training, and debugging your model), and serving (testing, deploying, maintaining).

Coding Resources

https://pgexercises.com/
My go-to for someone who wants to get familiar with SQL or freshen up for a screen (besides hackerrank). Recursive, strings, and dates probably aren’t really that necessary for a technical screen.

https://adventofcode.com/
Fun way to freshen up on any coding language (python github repo with some of my code practicing here).

https://www.kaggle.com/datasets
Great datasets to work with and freshen up on ML / view notebooks, etc (e.g. for github).

https://www.youtube.com/c/joshstarmer
Best overview of many, many Stats/ML concepts on the web IMO.

Interesting Business Stuff

https://www.scarletink.com/p/how-companies-incentivize-layoffs
Interesting perspective that’s come up a lot in 2022 since ZIRP ended
Obviously, competence (and keeping your peers / leadership happy) is important for your career as well, but it does mean that your primary career driver is not to be the best at delivery. It’s to be the best at growing your team. [when promos are based on HC]

https://www.brex.com/journal/the-enduring-value-of-funding-driven-fintech
Great overview of ZIRP ending on fintech by Michael Tannenbaum, an exec I worked with at SoFi. Also reminds me of how a lot of funding-driven tech companies often IPO to cash out/pay the team, when really an IPO was supposed to be about opening funding to the public.
However, as interest rates hovered near zero over the next decade and so much of my time was spent at SoFi (asset-based in the form of student loans, personal loans, mortgages) and Brex (fee-based in the form of charge cards and SaaS), I forgot the economic value of deposits!

https://alexdanco.com/2020/02/07/debt-is-coming/
Interesting view of the interplay of business and funding.
The view of production capital is exemplified by Peter Drucker: ‘Securities analysts believe that companies make money. Companies make shoes.’”
…
However, investors and operators are often deeply misaligned: investors think in bets, while operators think in consequences.

https://www.forbes.com/sites/paultassi/2023/01/17/netflix-has-created-a-self-fulfilling-cancelation-loop-with-its-new-shows/?sh=7dc05aa3784d
Interesting Forbes article that highlights what I would consider Netflix running up against ‘Goodhart’s law’ (“when a measure becomes a target, it ceases to be a good measure”) – ie that targeting early viewership is resulting in shows getting cancelled early and creating a self-fulfilling loop.
The idea is that since you know that Netflix cancels so many shows after one or two seasons, ending them on cliffhangers and leaving their storylines unfinished, it’s almost not worth investing in a show until it’s already ended, and you know it’s going to have a coherent ending and finished arc.

Random Categories

https://blog.xaviershay.com/articles/a-system-for-email.html
Great way to think of email to reduce overwhelm.
Your inbox is not a to-do list. It is not a calendar. It is not a passive information source. It is good for one thing and one thing only: triage.

https://forums.t-nation.com/t/dispelling-the-glute-myth/284458
This website has gone way downhill, but this article changed my athletic career. Bret Contreras introduced me to hip thrusts, which is like the bench press but for your glutes. Side note: another exercise adage I’ve tried to remember wrt join health: motion is lotion.
Most experts don’t know shit about the glutes. Despite the fact that the gluteus maximus muscles are without a doubt the most important muscles in sports and the fact that strength coaches helped popularized “glute activation,” no one has a good understanding of glute training. Bodybuilders, powerlifters, and physical therapists think they do, but they don’t. In fact, the experts are so far off the mark that their best glute exercises can only activate half as many fibers as the glute exercises I’m about to show you.

https://www.upworthy.com/bartender-explains-why-he-swiftly-kicks-nazis-out-of-his-punk-bar-even-if-theyre-not-bothering-anyone
Great example for the ‘tolerance paradox‘ – that is:
Karl Popper describes the paradox as arising from the self-contradictory idea that, in order to maintain a tolerant society, the society must retain the right to be intolerant of intolerance.

Memes & Gifs

For Wednesdays

For Fridays: https://www.youtube.com/watch?v=kfVsfOSbJY0

For stakeholders with ridiculous expectations: https://www.youtube.com/watch?v=2wA-8yrjEpQ
I want everything in one bag, but I don’t want that bag to be heavy