Miscellaneous unsolicited (and possibly biased) career advice | Hacker News Reader

Miscellaneous unsolicited (and possibly biased) career advice

erikbern.com

101 pointsheroHACK1729 comments

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.

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.

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.

> 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.

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.

My impression is the exact opposite. As an intern, you want to learn from a company that actually has something to teach, not from one that cuts corners and is way too small, inexperienced and busy to spend time on guidance. But as a junior developer, you can grow with a fast growing company. Not everybody succeeds at that, but if there ever was a good opportunity for it, it's at a fast growing company.

> 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.

Anecdata: junior engineer going through this process right now. Hard to move to a new company due to the specificity of the skills I developed and can't move up as they just bring in new people.

tried going to networking events for years. didn't do anything. the only thing that actually made a difference was to give talks myself and help people (without letting myself be taken advantage of)

"Choose" assumes we have a choice.

"Choice is of the free. When no choice presents before me, I'll make one myself."

Just throwing some inspirational BS

If this was health advice instead of career advice, it would read as

* Eat less

* Move more

* Consume in moderation

* Socialize

These pieces of advice may not be immediately obvious, but they're also incredibly unhelpful.

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.

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.

> 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.

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.

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.

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.

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

Any recommendations for somebody who ignored stats more than he should have as an undergraduate and wants to try to catch up? I've gone through and worked all of the exercises in my old calculus textbook so I have a good (fresh) handle on calculus now. Where's the best place to go next?

Understanding the distributions helped me the most. My favorite class was something like ‘single point estimates’ because it covered a lot about why mean and std dev are so powerful, but also sometimes so broken at conveying meaning.

Depends on the context for trying to catch up; both where you're at in life and what you're wanting to get out of it.

If you want to connect, feel free to reach out to the email in my profile. I may not be the best resource for best places to go next depending on what you're after, but may be able to help out.

A few general points though:

- Every field has it's own flavor of statistics. Supply chain, marketing, industrial engineering, business operations, finance, etc. Most practitioners will bastardize a technique or methodology common in their field before reaching out to another one for something more appropriate. Keep this in mind when looking at things, as you can find a lot of cases where the general premise for a technique is no longer valid, but practitioners are still going on momentum. You can also find some really neat nuggets/advancements that can be generalized and applied to another field. Although this can be difficult to suss out, as every industry tends to develop their own vernacular to refer to a particular set of base statistical techniques.

- Focus less on the math and more on the applicability of a particular technique or methodology to a situation. Generally speaking, statistical techniques are nothing more than sophisticated heuristics. Their validity, applicability, and actionability are entirely dependent on the situation they're applied in and the particular heuristics (statistical techniques) chosen. Understanding the techniques that are out there, what their applications are, and what their limitations are is far more useful than focusing purely on the math. The math can always be looked up once you know what to look up.

- Design of experiments[1] is a critical and often overlooked concept. It's rarely done in practice, and even when it is it's rarely more than a superficial attempt. But is a hugely important concept to understand how to approach a problem space.

- The output of a statistical analysis ranges from "checks the box of measuring something but so disconnected from observed reality that it'll otherwise be ignored" to "interesting but not robust enough to make decision on" to "directionally accurate" to "willing to make decisions based on confidence intervals". Understanding where your analysis stands on that scale, and where it needs to stand to meet your needs, is critical. Align your efforts with your needs, and set expectations accordingly.

[1] https://www.jmp.com/en_ch/applications/design-of-experiments...

For years I thought I had to do it the way the big boys do, studying university text books and referencing papers in the field and putting a bunch of mathematical notation in my designs and emails. While also knowing all the foundations underneath. Unsurprisingly, that didn't really go anywhere, except hours lost fiddling with Word's equation editor (or worse, Latex).

What did help me was reading a few (really a few - just 2 or 3) simple, applied books; a 'statistics for dummies' (literally, the 'for dummies' book), a textbook used in undergrad business courses ('<something something> business analytics' I think?) and a book that applied all the stats to the field I was working on at the time (transportation modeling). Just being able to apply a linear regression (as in, actually being able to estimate the parameter on a single regressor in a simple data set) got me much further than all the times I thought 'whoops, getting into optimization now, better put this aside and first get a graduate level understanding of linear algebra'. And in a week instead of 2 years, too - quite important to keep your motivation up when you're not a full time student any more.

So while the above is not 'advice', it is my personal experience that when learning applied maths at a later age, it was better for me to focus on application and taking shortcuts even if that meant not fully knowing or understanding what was happening underneath - as intellectually unsatisfying and 'dirty' that felt at the time.

Also went to school for statistics and wanted to echo this. There's some neat stuff in the field. It has zipitty-doo-dah to do with "statistics" in the common parlance. A lot of businesses would be better off if everyone one day forgot what the "%" character meant.

This. My two biggest takeaways from years of stats classes were that, when applied right, you can do some really fascinating things with statistics. And that no one ever applies things right, so take any stats you come across with a grain of salt.

After years of professional experience, those are still the two biggest takeaways from stats. The only thing that's been added to the list is the fact that more often than not, statistics/data science is just a political tool to justify budget increases and absolve decision makers of culpability.

Also suggest reading "How to Lie With Statistics".

Classic, short book about fundamental stats concepts everyone should know.

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...

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://erikbern.com/2018/02/15/new-benchmarks-for-approxima...

What are fast growing orgs/industries of today?

upvote for learning stats. very valuable.

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