在手机上保存，获得更好体验
本字幕由TME AI技术生成
Welcome back to the deep dive
Today were uh really digginto a career path
That kind of turns traditional ideas on their head
Yeah
It's a fascinating journey
Honestly we've got this detailed interview transcript with someone we're calling xy quite path is what was pretty onlinear yeart out placin in english major
You know product manager at a top health tech company and senfrancco quite depivit
So our mission today is to really unpact this strategic moves
Those deliberchoices
What they actually called the twenty percent effort
Yeah
They made this leap from liberal arts
But it's
It's this vananed quanantatative tech rules possible and let's ground this first
And where they are now like you said apm in the senfrances co taxine
It's demanding definitely demanand they mentioned work stress gets up to a what seven eight
Eight at ten
Ten ah specispecially around product launches
It comes in waves
But when it hits it's intense
But what's really interesting
I think is how they manage that it's this incredibly strict separation between work at life
Well absolutely once worwork has done
It's like a switch flips complete focus on these other quite demanding hobbies like learning spanish right and social dancing
Yep
Saua and linddy hob plus a really serious commitment to physical health
It's structured disciplined time off
Which seems well
It seems to clash what their core philosophy about success doesn't it
It really does they state pretty firmly
That success is eighty percent luck
Twenty percent effort
So it's mostly luck
Why all the intense disciplined and planning that's the paradox
We need to explore what is that twenty percent effort exactly
And to understand that we probably need to go back way back to what they call the dark years high school right a time
They said they actively try to forget
And 你的对的
Profoundly unhappy
And it wasn't really about the school work itself more the environment there was this lack of internal drive
You know couldn't see how the daily grind connected to anything meaningful down the road and a huge external pressure too especially from the mother extreme pressure in youfour seven study focus no phone no brakes
Not even really during lunch
They described it as just sufficcaating constant tension
Wow
And then came that bigerly step back
Yeah the failure to get into the high dschool's experimental class
But at the time felt like the end of ds world for them especially since most of their friends got in right right like eighty percent of their middle school
Here so it was a huge psychological blow felt like a massive failure devastating then flooking like their prospective shifted completely
Totally
They now see those kinds of small failures in youth
Like missing out on that class as actually beneficial
Essential
But how so beneficial house for building resilience
It taught them mabe the the hard that these early ly tbacks aren't
The end all bill high school achievements are just one small step gave him perspective
I guess help them move past it and focus on the bigger picture exactly
And that bigger picture that was largely insurance by the father uh okay
Well the mother ther focus on the data day academics
The father was playing the long game
Laying out a strategic plan for college years ahead
What did that plan look like like key things
Things ttarted a nine eighty five comprehensive university not just for the name
But specifically for the envirment right laacout a diverse majors right inspired by historical examples like southwest associated university
They mentioned precisely the platform was crucial
And second the foundation had to be math
Math okay
That's the critical choice like math specispecially not intellecial business given the fininterest strategy
Deep strategy
The father saw math as the essential tool pait the uh intellectual insurance for more advanced fields later on was like financial engineering or actual al science
The exactly things that definitely required graduate study
So wasn't about getting a job right out of undergrd
It was about building serious foundation in rigor for the long hall that makes less sense optimize for the decade
Not the first giall yes was lucky
Because the initial planted to a nnag right yeah despite all the planning they got placed into the english major initially but kind of professional adjustment or allocation
A detor
A detyeah
But because that mathegwas already locked in
Because just first couple semesters weren't really about learning english
Thewere about planning the transfer out of english
Exactly preparing to pip it okay
So let's talk about those two semesters in english
Because ah their assessment is well predessing it really is they basically saw the whole major
Because just training those basic ahrw skills
Listening
Speaking
Reading
Writing
And they felt university wasn't a place for that yeah strong opinion there they argued it's a poor career investment learn language better through emersion ababor or technology takes over right
These specifically brought up things like chat gpt arguing that tech can handle translation and a lot of basic language tasks making those purely language focus skills less valuable long term
So the entire four year engsh sh degree in their view boil down to pretmuch achieving a hundred plus on the tofl unnecessary hurdle may be for studying abroad
But not a deep competitive advantage in itself
Wow
That is a harsh take especially from someone
Who is actually in the program
Even briefly was there anything positive they took away
Unneprisingly yes one thing the english writing classes made them anallize american movies and tv shows interesting
How to that help it gave them a significant insight into american culture and world view
But sort of cultural bridge they not not across otherwise
Which probably helped a lot later when they move to the us okay small overlining them
So they make the switch was there anything department pop five nationally you set yeah
And the difference was a motiate yege ramp up and rigger
What did that look like what kind of math we talked a serious stuff
Three semesters of mathematic analysis
That's like super theoreticical proheheavcalcuculubut seseststof advanced algega c they like if the god basic linear algebra and even pure math right like togemaa yeah
Topology number theory
The really abstract stuff how on if the goal was applied feels like financial engineering why dive so deep into pure abstract math h seems a tertertututive
But they actually
Tually trainexgoing through ppure math courses made it crystal clear to them
That they were not cut out to be a research mathematician
Ah okay
So it's negnostic in a way partly yeah the understood
It could follow the logic
But didn't feel they had that spark that creativity for solving really openced abstract problems
But the main point wasn't necessarily to become a pure mathematician
It was about the training
Exactly the mental training
Which really kicked in
When they moved into the ablied traacbut demasiinous mamatic raa sesis
Yeah
The stuff that directly feeds into financial math operations research statistical math too
And this i think is the absolute core take away for you listening that math training
It fundamentally changed
How they thought completely reshaved it totally it built this this top down highly logical uh deductive way of thinking like a framework
Exactly a framework
How they approach problems now how does that actually show up like in their current job
They give an example
When faced with a complex business issue
They instinctively break it down
Ok
Three points for a second third
Its structured analytical comes directly from that math proof training deconstruct the problem logically precisely less reliance on just intuition more on structure and testable components
It became their intellectual signature
Really
And they're final take on the whole university experience very happy
No complaints about the university or the math major
That strategic choice
That foundational bigger absolutely paid off
It built the capital for the next big step
Which was grad school masters
An operation research
A conmbia
A yorority
Okay
So now executing the original plan moving towards financial engineering right applying that math rigger two finance yep focusing on statistics probabilities tocastic processes simulation all the fe building blocks
But another pivot happened always builving
What changed this time the market partly
But also their perspective most classmates were heading into traditional financiers management quantlls
But they felt drawn more tords tech
This is yorks start up scenen was buzzing
So it pulled them
So the fofocushishifted from financial engenering modeling to data analysis
Which let them straight to the data scientist ds track
And they had a very specific break down of d roromodethey think this forit it arly from data engineers who builbuilthe the infrastructure
Yeah
Very practical definitions based on workplace functions
Three main types they saw okay was the first son first and they
They hihiest and demand is the product oriented tax
This is the person who takes the product managers business questions
Often kind of vague and translates them into concrete data questions to find answers like why did users drop off here yeah
And the second type modeling
These folks focus purely on optimizing the models themselves
Which they fincidation engines for assting models tuning them to hit business goal
Got it more teactical optimization
And the third is the machine learning engineer or m ali job is crucial making
Sure the modedels built by the modelers can actually run reliably and scale up in the real world in production the bridge from tearing the practice
Exactly they make it reproduciable and scale
And they are careful to distinguish these from seararscicitist rright
Oh Yeah
People working on like foundational deep learning the corore tech behind something like chat gpt
That's a whole separate track more academic usually neea a phd okay
So they pivoted into this ds world
是的是的
Was it a smooth transition given the math background not entirely here's where that twenty percent effort really shows up again
They explicitly mentioned that comcomfrom pure mamath
They lacked programing skills uh the practical tooling
And
And that caused a lot of difficult to especially in the first year of grads school a steep painful learning curve to catch up on the coding side
That's such a key point
The theory gives you the mindset
The framework
But you still that a grin to learn the tools absolutely
And that struggle interestingly led towards the final pivot into the product managt
It had that come about it
It heavavinfluenced by a specific person
A menting figwho was was forformer head of product at their company
What was special about this mentor
They just found this person incredibly smart
Super helpful and crucially
Their way of thinking really resonated
It clicked with that logical structure approach they built through math
So seeing that kind of thinking applied at the product strategy level was inspiring
Exactly the pm role felt like the natural end point
It let them use that top down logical framework not just for data problems
But for broader business strategy user needs the whole picture
It's like the syntheis of the mah logic and the data science application perfectly put apm takes those big messy and bigguous problems
How do we grow
What should we build next
And apply that structure ture breks it down
So the ds and engineering teams can actually execute ok lelet circle back that core philosopy now eighty percent luck
Twenty percent effort
Aha after hearing the whole story
How do they define that huge eighty percent luck
Component it boils down to access
Access to high quality platforms
So atforms like good parents who provided that initial strategic vision
The platform of a top university like columbia and being n yc
During that take expansion and critically meeting the right people
Specific mentors like that head of product and the engineers who help them bridge the programming gap
So luckisn't just random chance
It's being in the right environments with the right support that seems to be their definition yeah platform preparation people and thinking about advice for their younger self back in those dark years of high school
Their answer was pretty stark almost nialistic like he said basically no advice at all
Why because i argued a fifteen year old
That wouldn't understand the perspective just isn't there yet
So the real wisdom wasn't
Something you can tell them back that it was something they had to learn through the experience
The main lesson not to be too immersed in your own small failures
It high school catastropy is just noise in the long run perspect tive okay
So where really highit know whathe short ort terananme seems like the next two or three years are about continuing up the corporate latter as apm then apppaof a assessment to do it to consously figure out how to integrate the career path with personal life goals like potentially starting a family
It's still strategic planning just applied to life gogols the flexmization problem
You could say that ultimately though the big leleability from whowhole ourney this deep dive so really highlights the power of those foundtional skills
That logical conductive thinking home to by mah
Exactly it aclike career insurance durable it gave them the flexibility in the confidence to pive it across really different high value
You feels fedspm fields that are constantly changing
And that flexibility brings us right back to the beginning
Doesn't to their really quite strong dismissal of academic language study yeah and grounding that dismissal in the rise of ai and tools like chat gp ory
It creates a direct challenge based on their own success story
It pivoted away from a sill
They saw w vulnerable towards foundation tional logic and thriwhich
Which ves us
Us with the listener
With a pretty provocative thought to to w on right
So you it is given this successful shift and xy is explicitpoint about technology automating things like routine translation or a simple data reporting
What is the real strategic long term value
A focusing your education on skills that seem increasingly likely to be commoditized by ai
It forces you to ask right
Are you building durble knowledge in ways of thinking
Or are you acquiring skills that might have a shorter shelf life
It's a critical question for planning your own next strategic move
