5 ms·

Miscellaneous unsolicited (and possibly biased) career advice

101 points by heroHACK17 7y ago

peter_l_downs 7y ago
I would highly recommend subscribing to Erik's rss feed or email drip or whatever, his blog posts are always high quality and have been very useful to me when it comes to "career thinking." And fun fact, he wrote Annoy, which is damn good software. https://github.com/spotify/annoy https://github.com/spotify/annoy https://erikbern.com/2018/02/15/new-benchmarks-for-approximate-nearest-neighbors.html https://erikbern.com/2018/02/15/new-benchmarks-for-approxima...
ropiwqefjnpoa 7y ago
This one I think is valuable for all ages: "When you’re young, care more about building human quickly and not so much about financial capital. The human capital will pay much larger dividends over your lifetime." I've worked from home quite a bit over the past few years and I've begun to notice it affecting my personal/communication skills. I'm actually trying to get back into the office more often.
- algaeontoast 7y ago
  Having worked remotely for a startup and within a really nice office for a corporate hell hole, I'm now convinced that the best balance for productivity / sanity for me is in-office 4 days a week with a handful of remote days. When I was fully remote, after a few weeks I joined a really unique co-working space that was really more of a social club (Hall Boston - now defunct for any curious). It was a really special place, but in hindsight I didn't get much work done when I was focusing there. However, gives me hope for a resurgence of community driven social clubs for city dwellers and entrepreneurial types.
SamuelAdams 7y ago
> Statistics. Seriously, I really wish I had studied more of it in school. Basically goes for anyone in the STEM field, IMO. I really wish more people invested in statistics and data analysis classes. People take you more seriously in a business setting when you can say "Email A resulted in a response rate of 80%". I usually hear "We think Email A is better because we feel it in our gut". Ok, not those words exactly, but that's the point. Looking at data, understanding it, and directly applying it to your job is a hugely underrated skill.
- holy_city 7y ago
  A bigger issue I've seen is that "gut feeling" comes from a misplaced sense of confidence. Like say, sample size. I can't tell you how many times I've heard engineers say "the data isn't significant because the sample size is too small." If you have the data, calculate the confidence interval! Most of the time you don't need hundreds to thousands of data points to be reasonably confident, just a few dozen. I remember the example distinctly from my sophomore engineering stats course, I don't know why everyone else has forgotten it.
  - opportune 7y ago
    In my experience when the data is very small it is almost always also biased towards how easy it was to gather, which also makes it non representative. Think about it, if it were as easy to let n=5000 as it were to let n=25, you would always pick 5000. You only pick n=25 because of the low effort involved, which often means proximity. A very common example is when some software feature is A/B tested only internally, or even only tested on the team that developed it. It introduces a lot of bias in users’ technical competence, willingness to understand/understanding of the new behavior, how the environment is set up, etc.
  - m12k 7y ago
    I think most people overlook/don't know that the needed sample size depends not just on the confidence you want, but also on how big the effect you want to measure is. E.g. if landing page A has a conversion rate of 50% and B has one of 55%, that's going to take a lot of sampling to prove. But if A has 40% and B has 80%, then that's going to show up in the samples very quickly. But exactly which question you ask affects the needed sample size greatly - e.g. showing that 'B performs better than A' will take fewer samples than 'B performs at least 20% better than A'. This makes it much harder to have a correct intuition about needed samples sizes.
- cosmie 7y ago
  I went to school for statistics, and work in "analytics" (a catch all term for anything from basic reporting to analytics infrastructure to conversion rate optimization experiments). You're absolutely right in that people tend to take you more seriously if you come with numbers. But it's such a kangaroo court[1] that it drives me nuts. The instrumentation and implementation to support that sort of data-driven approach is usually far too lacking to give it the amount of merit it receives. Once you take do a first principle's sanity check of things, you learn that no one on the business side has a solid understanding of what "response rate" is actually referring to. Then when you look at the technical implementation, you realize that there's little reconciliation between what it's actually representative of and what anyone on the business side think it's representative of. Never underestimate someone's gut feeling, especially so if it's from an individual in the trenches. More often than not, dissonance between gut feelings and data point to an issue with the data. Not necessarily that the data is wrong, just that it isn't fully representative of the context it's being collected in and should be trusted accordingly. [1] https://en.wikipedia.org/wiki/Kangaroo_court https://en.wikipedia.org/wiki/Kangaroo_court
chimi 7y ago
In summary: network a *lot* Choose fast growing organizations Choose people you can learn from Enter a market with few smart people Use your smart connections to dominate that market The key will be entering a market without a lot of smart folks in it, while also choosing the group in that market that is smart. The problem is if most folks in a market aren't smart, then those who are really changing that market look dumb to those already in it, so are the pioneering minds there smart or re-inventing failed wheels.
- phnofive 7y ago
  Networking is the only staging step on the list, which supports the titular concern about bias. Every other condensing step requires good judgement and luck, which isn’t helpful as generic advice.
  - algaeontoast 7y ago
    Joining a fast growing org early in your career is a bad idea, doing it as an intern is indispensable. Also, networking is vital, however mostly to develop a filter for BS and BS slingers (money slingers or "rich" people are deceptively good at BS slinging). I had a friend who would go to meetups and befriend everyone, in my experience this is not really productive and will lead to being taken advantage of.
    - r00fus 7y ago
      > Joining a fast growing org early in your career is a bad idea, doing it as an intern is indispensable. Isn't being an intern "early in your career"? I don't understand the logic above.
      - goatinaboat 7y ago
        No, being an intern is prior to your career beginning. If you are a junior engineer in a fast growing org you will work your ass off, being forced to cut corners to ship quickly, then rather than promote you the org will simply hire people above you. And you will find that what you learned was not a solid foundation to move onwards.
joshuaellinger 7y ago
upvote for learning stats. very valuable.
silentsea90 7y ago
What are fast growing orgs/industries of today?
bjornsing 7y ago
I think this fear is based on a huge overestimation of the marginal utility of raw intelligence in the political/power field. When there’s a coup somewhere it’s usually not the math department that rolls triumphantly through town. Oppenheimer was a superintelligence compared to his rivals in the military industrial complex, and we know how that turned out. I could go on, but you get the point. I think what we should be more worried about is the power dynamics/balance between groups of people in society. The relatively morally bankrupt “power people” within the military still needed the cooperation of people like Oppenheimer to get their hands on the bomb. That constraint could soon be gone...

101 points by heroHACK17 7y ago

I would highly recommend subscribing to Erik's rss feed or email drip or whatever, his blog posts are always high quality and have been very useful to me when it comes to "career thinking." And fun fact, he wrote Annoy, which is damn good software. https://github.com/spotify/annoy https://github.com/spotify/annoy https://erikbern.com/2018/02/15/new-benchmarks-for-approximate-nearest-neighbors.html https://erikbern.com/2018/02/15/new-benchmarks-for-approxima...

ropiwqefjnpoa 7y ago

This one I think is valuable for all ages: "When you’re young, care more about building human quickly and not so much about financial capital. The human capital will pay much larger dividends over your lifetime." I've worked from home quite a bit over the past few years and I've begun to notice it affecting my personal/communication skills. I'm actually trying to get back into the office more often.

algaeontoast 7y ago
Having worked remotely for a startup and within a really nice office for a corporate hell hole, I'm now convinced that the best balance for productivity / sanity for me is in-office 4 days a week with a handful of remote days. When I was fully remote, after a few weeks I joined a really unique co-working space that was really more of a social club (Hall Boston - now defunct for any curious). It was a really special place, but in hindsight I didn't get much work done when I was focusing there. However, gives me hope for a resurgence of community driven social clubs for city dwellers and entrepreneurial types.

SamuelAdams 7y ago

> Statistics. Seriously, I really wish I had studied more of it in school. Basically goes for anyone in the STEM field, IMO. I really wish more people invested in statistics and data analysis classes. People take you more seriously in a business setting when you can say "Email A resulted in a response rate of 80%". I usually hear "We think Email A is better because we feel it in our gut". Ok, not those words exactly, but that's the point. Looking at data, understanding it, and directly applying it to your job is a hugely underrated skill.

holy_city 7y ago
A bigger issue I've seen is that "gut feeling" comes from a misplaced sense of confidence. Like say, sample size. I can't tell you how many times I've heard engineers say "the data isn't significant because the sample size is too small." If you have the data, calculate the confidence interval! Most of the time you don't need hundreds to thousands of data points to be reasonably confident, just a few dozen. I remember the example distinctly from my sophomore engineering stats course, I don't know why everyone else has forgotten it.
- opportune 7y ago
  In my experience when the data is very small it is almost always also biased towards how easy it was to gather, which also makes it non representative. Think about it, if it were as easy to let n=5000 as it were to let n=25, you would always pick 5000. You only pick n=25 because of the low effort involved, which often means proximity. A very common example is when some software feature is A/B tested only internally, or even only tested on the team that developed it. It introduces a lot of bias in users’ technical competence, willingness to understand/understanding of the new behavior, how the environment is set up, etc.
- m12k 7y ago
  I think most people overlook/don't know that the needed sample size depends not just on the confidence you want, but also on how big the effect you want to measure is. E.g. if landing page A has a conversion rate of 50% and B has one of 55%, that's going to take a lot of sampling to prove. But if A has 40% and B has 80%, then that's going to show up in the samples very quickly. But exactly which question you ask affects the needed sample size greatly - e.g. showing that 'B performs better than A' will take fewer samples than 'B performs at least 20% better than A'. This makes it much harder to have a correct intuition about needed samples sizes.
cosmie 7y ago
I went to school for statistics, and work in "analytics" (a catch all term for anything from basic reporting to analytics infrastructure to conversion rate optimization experiments). You're absolutely right in that people tend to take you more seriously if you come with numbers. But it's such a kangaroo court[1] that it drives me nuts. The instrumentation and implementation to support that sort of data-driven approach is usually far too lacking to give it the amount of merit it receives. Once you take do a first principle's sanity check of things, you learn that no one on the business side has a solid understanding of what "response rate" is actually referring to. Then when you look at the technical implementation, you realize that there's little reconciliation between what it's actually representative of and what anyone on the business side think it's representative of. Never underestimate someone's gut feeling, especially so if it's from an individual in the trenches. More often than not, dissonance between gut feelings and data point to an issue with the data. Not necessarily that the data is wrong, just that it isn't fully representative of the context it's being collected in and should be trusted accordingly. [1] https://en.wikipedia.org/wiki/Kangaroo_court https://en.wikipedia.org/wiki/Kangaroo_court

chimi 7y ago

In summary: network a *lot* Choose fast growing organizations Choose people you can learn from Enter a market with few smart people Use your smart connections to dominate that market The key will be entering a market without a lot of smart folks in it, while also choosing the group in that market that is smart. The problem is if most folks in a market aren't smart, then those who are really changing that market look dumb to those already in it, so are the pioneering minds there smart or re-inventing failed wheels.

phnofive 7y ago
Networking is the only staging step on the list, which supports the titular concern about bias. Every other condensing step requires good judgement and luck, which isn’t helpful as generic advice.
- algaeontoast 7y ago
  Joining a fast growing org early in your career is a bad idea, doing it as an intern is indispensable. Also, networking is vital, however mostly to develop a filter for BS and BS slingers (money slingers or "rich" people are deceptively good at BS slinging). I had a friend who would go to meetups and befriend everyone, in my experience this is not really productive and will lead to being taken advantage of.
  - r00fus 7y ago
    > Joining a fast growing org early in your career is a bad idea, doing it as an intern is indispensable. Isn't being an intern "early in your career"? I don't understand the logic above.
    - goatinaboat 7y ago
      No, being an intern is prior to your career beginning. If you are a junior engineer in a fast growing org you will work your ass off, being forced to cut corners to ship quickly, then rather than promote you the org will simply hire people above you. And you will find that what you learned was not a solid foundation to move onwards.

joshuaellinger 7y ago

upvote for learning stats. very valuable.

silentsea90 7y ago

What are fast growing orgs/industries of today?

bjornsing 7y ago

I think this fear is based on a huge overestimation of the marginal utility of raw intelligence in the political/power field. When there’s a coup somewhere it’s usually not the math department that rolls triumphantly through town. Oppenheimer was a superintelligence compared to his rivals in the military industrial complex, and we know how that turned out. I could go on, but you get the point. I think what we should be more worried about is the power dynamics/balance between groups of people in society. The relatively morally bankrupt “power people” within the military still needed the cooperation of people like Oppenheimer to get their hands on the bomb. That constraint could soon be gone...