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  • (upbeat country music)

  • - This is one of those off-beat talks

  • so we're gonna expect a lot of energy.

  • Do the wave or something, we'll see.

  • Welcome to Moneyball on the Keyboard,

  • Scouting Talented Developers.

  • I really do appreciate that you're joining me

  • at this time slot, I know there's some awesome talks

  • and so I appreciate you being here,

  • because without you there's no talk.

  • I am Adam Jonas, I'm the Managing Director of Engineering

  • at the Flatiron School where our primary goal is

  • to inspire people to fall in love with code.

  • We train some of the best Junior Developers around,

  • placing them in awesome companies like Kickstarter,

  • Etsy, Intel, and the New York Times.

  • I am not a teacher, my team has been working

  • on transforming our internal learning management system

  • into a full-fledged education platform,

  • it's called Learn.co, we're really proud of it,

  • we'd love you to check it out.

  • But it's that work that really inspired me to

  • start putting together these slides,

  • because I spent a lot of time thinking about

  • how we could create some sort of metrics around

  • how we could possibly surface talented students.

  • And so as I often do, I fell back to a sports analogy.

  • And so today we're gonna talk about baseball.

  • We're gonna use baseball in the ways that

  • talent is identified in baseball

  • as an example of how we can improve our own mental model

  • for thinking about talent and software.

  • And so why am I in particular talking about baseball?

  • It's because, thanks to the Flatiron School,

  • software is actually my second career.

  • I worked in a few different capacities in scouting,

  • player development for the Brewers, the Twins,

  • and then ran an academy and spent most of my 20's

  • doing this, living in and around Latin America,

  • and specifically the Dominican Republic.

  • I moved back to the U.S. in 2010

  • and tried my hand at founding a company

  • that helped drafted players better improve

  • their bargaining position,

  • make more informed decisions about their futures.

  • And while this venture went terribly, horribly wrong,

  • it did pique my interest in code, which eventually

  • landed me at the Flatiron School.

  • And so here's what we're going to go over today.

  • I'm going to tell you a little bit about

  • the Moneyball Philosophy

  • and why it created such a shift in baseball.

  • Then we're all going to go to Scout School together

  • and see how professional baseball talent

  • has been traditionally scouted.

  • And after that we're going to see how talented students

  • are identified at the Flatiron School.

  • And finally, based on what we've learned, we're gonna

  • discuss three ways to better understand developer talent

  • and how to better evaluate it going forward.

  • If you're still awake at that point,

  • we'll hopefully do some Q&A at the end.

  • Cool, one more thing before we dive in.

  • And there's lots to cover, but I think it's natural to

  • sort of think about these sorts of conversations

  • specifically when thinking about hiring.

  • But we evaluate talent all the time, we evaluate

  • our coworkers because it has a lot to do with compensation.

  • We evaluate our coworkers because it has a lot to do

  • with clout on our team.

  • We evaluate open source contributions, and pedigrees,

  • and everything that comes across on a resume.

  • We evaluate speakers and whether they're any good.

  • We put value judgements on a lot of things and so

  • I think we're wired to do that, and that's totally okay.

  • But I challenge you to just not limit your thoughts on this

  • subject to just the interview process

  • because it's a lot bigger than that.

  • On with the show.

  • So you might be familiar with Moneyball,

  • it was a book written in 2003 by Michael Lewis,

  • and then turned into a movie with

  • the always handsome, Brad Pitt, in 2011.

  • And the essence of Moneyball

  • while specifically focusing on baseball, is really about

  • objectifying what was previously thought as subjective.

  • And so the subject of Moneyball is the Oakland Athletics,

  • I can see one of those hats right there, awesome.

  • And the fact that they have smaller revenues

  • and constrained resources

  • against other teams they're competing against

  • on that right side of the graph, the New York Yankees,

  • the Boston Red Sox, et cetera.

  • And it's because of those constraints that Oakland is forced

  • to search for undervalued players in the market.

  • And they end up turning to these under utilized tools

  • of statistical analysis.

  • And so what have we learned from the Moneyball approach?

  • We learned that industry outsiders who had not been

  • indoctrinated with the traditional way of doing things

  • could identify inefficiency in the old ways and

  • we learned that we could use better talent analysis tools

  • to determine a players objective value.

  • So, for an example,

  • what's the difference between a player who

  • gets to first base via a single,

  • and a player that gets to first base via a walk?

  • Intuitively, we favor the person who took the action,

  • the person that swung the bat

  • and earned their way to first base.

  • But from a team's contribution perspective

  • it's about the same.

  • You got a guy on first base, that's about all it is.

  • And so, when we think about these contributions,

  • we often will overvalue what we think is being earned

  • or somehow is, has more merit to it.

  • But, and this is exactly what was happening in baseball.

  • We saw players with high batting averages

  • were being overvalued, and players with

  • high walk percentages were being undervalued.

  • So, the Moneyball approach took advantage of this,

  • this gap between perception and reality

  • and it was more accurately able to value

  • a player's contributions to the team.

  • So, this is Moneyball, it's this objective view,

  • this data driven view, this high-level view,

  • this detached view.

  • But it's not perfect because humans are complicated.

  • And so let's turn to a little bit more of a humanistic

  • approach by all heading to scout school together.

  • Scout school is a very real thing.

  • It's sponsored by Major League Baseball,

  • and I was lucky enough to attend in 2005.

  • And you have to be invited by a team,

  • and what you do is you spend two weeks spending the mornings

  • going over the theory of scouting and the afternoons

  • literally applying what you learned in the field.

  • Writing up reports and seeing different levels of the game.

  • And so, the first thing you learn is that all players

  • are evaluated in the five following categories called tools.

  • Hitting for average, hitting for power,

  • running, fielding, throwing, this is it.

  • This is what everything, everyone is evaluated on.

  • And players that possess elite talent in every tool,

  • well that's pretty rare.

  • Willie Mays stands out as sort of the prototypical

  • five tool player, that he could do it all.

  • But most players have strengths and weaknesses,

  • and it's these strengths and weaknesses that

  • help determine their role and the ways that they

  • can contribute to the team.

  • There's rubrics for all these tools as well,

  • and in Scout School they give it to you

  • in this neat little binder, if only it were that easy.

  • But, let's go over the running tool in particular.

  • And these grades range from two to eight,

  • as they do in all categories.

  • And it's measured by the time that the bat hits the ball

  • on contact to the time that they arrive at first base,

  • as measured by a stopwatch.

  • Now you can imagine there's some complicating factors here.

  • First of all, a righty is actually physically further

  • from first base than a lefty, also you would imagine

  • that baseball players being the people that they are

  • are not going to run 100% every single time to first base.

  • And so, we need to look for the right situations that

  • actually take down that time, lazy pop-flies,

  • gimme singles to left field, home run trots,

  • probably not the right time to do that.

  • And so, while traditional scouting has this reputation

  • of being purely subjective and sort of antithetical

  • to the Moneyball approach.

  • You can see how there's some standardization here,

  • you can see that there's possibly some consistency

  • and I think you'd be surprised at how consistent

  • the grades are from experienced scouts.

  • So the only way that this could work,

  • is if it were written down all the time.

  • And baseball has done an amazing job of leaving this robust

  • audit log for us to examine decisions over the years.

  • This is an actual scouting report,

  • you can see the rubric is there on the left.

  • It's not particularly hardened because it is

  • actually talking about all the tools.

  • You also have the five star categories that we

  • just went over as well as some other categories

  • to give you a little bit more context.

  • So this lets us know why we're hiring this person,

  • or why we're passing on this person.

  • It also let's us know where the candidate we see

  • as we evaluate the candidate, where are they today

  • versus where they could possibly project to the future.

  • Same player, a little bit more exposition.

  • And this provides a ton of data.

  • Not only data on the candidate themselves,

  • but data for us.

  • You know, this is not only about how we see

  • the person that we're evaluating.

  • It's also about the evaluator.

  • And so, this that means we're a lot less likely

  • to succumb to hindsight bias,

  • where we claim that we saw it all along.

  • At the end of the day a scout's job is to predict the future

  • and it's hard and it opens the scouts to a ton of criticism.

  • But leaving a record can be your proof,

  • and ultimately your redemption for naysayers.

  • I want to focus on three particular statements of this.

  • This is the "good face", this is the first one.

  • So, the "good face", pretty subjective, right?

  • Why is that even in the scouting report?

  • And the reason is that scouting, like a lot of other things,

  • is pattern matching.

  • And so this scout felt that he had seen faces like this

  • in the past, they could possibly be on an All-Star poster

  • or the face of a franchise.

  • And so he thought that this was data that

  • he would include in the report.

  • "Will be in the big leagues by age 21," pretty bold.

  • You're gonna go out on a limb for, this happens to be

  • an 18 year old, and say that they're gonna make it

  • to the highest level possible by the time

  • they can legally drink.

  • "All the basic tools to be an outstanding

  • "shortstop prospect, best raw tools of

  • "any position player I've ever scouted.

  • "Should only get better with maturity and experience,

  • "tools and makeup, to be a star."

  • This is in fact a report on Derek Jeter,

  • who was drafted sixth overall in 1992,

  • and he in fact did debut at age 21,

  • and he was a 14 time All-Star.

  • Which clearly he fulfilled the promise that was

  • predicted for him by the scout.

  • And so, if you think that this Derek Jeter

  • could possibly grow up to be this Derek Jeter,

  • then you're gonna want credibility for that,

  • you're gonna want someone saying that,

  • "I wrote this down, I put myself out there

  • "all those years ago, and guess what?

  • "I was right."

  • Let's look at three other examples.

  • Trevor Hoffman, future Hall of Fame pitcher.

  • Albert Pujols, the best hitter of his generation.

  • David Ortiz, cultural icon

  • and three time world series champion.

  • All these players were missed.

  • Trevor Hoffman was selected 288th overall

  • as a shortstop and then converted to a pitcher after

  • two seasons of proving that he could in fact, not hit.

  • Albert Pujols was drafted 402nd overall,

  • meaning that every single team had 13 opportunities

  • to select the best player and failed.

  • He was so good in fact, he was the fastest player

  • of his draft year to make it to the major leagues,

  • and he went on to win Rookie of the Year at age 21.

  • In 2002, which is the season that Moneyball takes place,

  • David Ortiz was let go by the Minnesota Twins

  • for no compensation and he was picked up

  • by the Red Sox the following winter

  • and hit almost all of his 500 home runs for them.

  • And so, how did this happen?

  • How did everyone miss these guys in an industry

  • so focused on evaluating every single player?

  • It's because they weren't seen in the proper context.

  • Their talent was hidden by their circumstances.

  • Hoffman was at the wrong position, Pujols was at a

  • junior college that had never produced a major leaguer.

  • Ortiz had a myriad of nagging injuries

  • which disguised his potential.

  • And so, let's take a step back and acknowledge

  • that talent and context are inextricably linked.

  • There is in fact, no such thing as general talent.

  • Derek Jeter earned more than $400 million

  • in his 20 year Major League career.

  • But it's not like you'd let him touch your vimrc file

  • or do your taxes.

  • His talent, your talent, my talent, everyone's talent,

  • they only exist in the context in which they're applied.

  • I want to extend this a little bit further.

  • Within the game, players are expected to contribute in ways

  • determined by their roles.

  • And so because of the defensive demands of a shortstop,

  • fielding, throwing, and running are considered

  • the most important tools for prospects.

  • If we look back at Derek Jeter's report,

  • you can see he comes in at a 65 on fielding,

  • a 60 on arm strength, and a 70 on running.

  • And his hitting and power grade out to 50's,

  • Major League average.

  • And so he's the exact profile for

  • what this scout is looking for in a shortstop.

  • And you can tell by his gushing report.

  • Then there's the first baseman,

  • the first baseman only works 100 feet away,

  • where the defensive demands of that position

  • are just not as critical.

  • And so they're expected to contribute in other ways,

  • namely to carry the offensive side of the coin.

  • Power, hitting, that's what they're known for.

  • And so it's really determined,

  • your value is only determined by the role

  • and the way that you can contribute to a team.

  • And these are generous, these are people that

  • are expected to contribute in multiple ways.

  • There's also the specialists,

  • in this case the specialist is the pitcher.

  • We only care about one tool of theirs, that's their arm.

  • How hard can they throw, how well can they throw?

  • How many outs can they get?

  • This is a picture of Jim Abbott,

  • and Jim Abbott had a 10 year Major League career,

  • he threw a no-hitter for the New York Yankees,

  • and he won a gold medal for team USA.

  • What makes Jim Abbott a little bit different

  • is that he was born without his right hand.

  • And so, if you met Jim Abbott on the street,

  • and tried to shake his hand, it might be difficult

  • to imagine that he is a professional baseball player,

  • let alone had this highly successful career.

  • But, it's because of the specialized nature

  • of his role and his unique abilities that he was able

  • to have this sustained success.

  • And so given that context is so important,

  • what's your context?

  • Are you looking for a generalist to work on a team?

  • Are you looking for individual contributors to work alone?

  • Is this a small start-up where

  • flexibility and adaptability are valued?

  • Or a large organization where

  • optimization is most important?

  • And so given that talent doesn't live in a vacuum,

  • getting a handle on your unique set of circumstances

  • and the circumstances that your team faces

  • is critical to understanding what talent really is.

  • Let me tell you a little bit about my context

  • and the way that we identify students

  • at the Flatiron School.

  • And I think, I sort of imagine this as a petri dish,

  • you can tell by my awesome graphic.

  • The Flatiron School has contact with

  • thousands of students a year, we have a 6% acceptance rate.

  • And so over the course of the last three years

  • we've accepted hundreds of students

  • and all of them that have been job seeking,

  • pretty much to a person,

  • has in fact, gotten a job.

  • And so we have lots of conversations with employers and when

  • I started talking about this to the admissions department,

  • I think I was sort of overwhelmed with the parallels

  • to my time in the Dominican Republic.

  • Where a lot of players had very little training,

  • and how do we know that they have talent,

  • before they really understand how to play the game?

  • And so, when we're tasked with trying to understand

  • who has talent and who doesn't, it's really difficult,

  • because you have very little context to go on.

  • And so what's their ceiling, how far can they go?

  • It's hard.

  • Remember the five tools of baseball.

  • These are the five tools of the Flatiron School.

  • Hireability, technical background,

  • aptitude, passion, and culture.

  • And so, before anyone has written a line of code

  • this is what we're looking for.

  • I want to go over these really quickly.

  • Hireability, our school's designed

  • for a very specific outcome.

  • And so when I say a successful student, I'm talking about

  • a student that can go from no experience

  • to employable in a really short period of time.

  • They can also get their first job at a high starting salary,

  • and they're promoted quickly, and their employer of course,

  • is giving us a lot of positive feedback on them.

  • And so, to be able to deliver on this promise

  • that we're making to our students, we need to know

  • that that outcome is actually something they want.

  • And so that's a big part of the admissions process.

  • We also need to know that they can hold a conversation,

  • that they can talk about times

  • that they worked together in a team,

  • that they can project enthusiasm and energy,

  • because all job interviews have

  • some of those components in them.

  • Technical background, it's exactly what it sounds like,

  • how much have they done in the past?

  • Aptitude, and you might see how some of these tools

  • can sort of bleed together,

  • sort of like a fast runner that can get down the line,

  • that might boost their hit tool,

  • because they can beat out some ground balls.

  • And so, how well do you think that this person can learn

  • is what we're actually after in aptitude.

  • How well can they integrate new information?

  • We happen to use tic-tac-toe as our code challenge,

  • and that's a highly Google-able challenge.

  • Lots of our students are just pulling down solutions

  • from the internet and so, part of what we do

  • is suss out the difference between what they actually wrote

  • versus what they pulled from somewhere else.

  • And I think that's okay, I think that's okay.

  • Because that's how I work.

  • You know, not every line of code, not every way

  • I attack a problem is from inside my own head.

  • And so we should be using the tools and the approach

  • that we expect to be using after they graduate.

  • And so, what we're looking for here is pattern matching.

  • Can they see sameness and differences?

  • What's the tiniest next step that they can take to improve?

  • Here's our rubric, and it ranges from one to five,

  • starting with low effort and little comprehension,

  • going all the way up to clean, object oriented code

  • that's tested and clearly demonstrates

  • that she's mastered the problem.

  • We have exemplars for all these answers.

  • And again, like baseball scouts I think you'd be surprised

  • by the consistency from instructor to instructor

  • in their grades.

  • Passion.

  • So there's this sort of Sisyphean nature to what we do.

  • because when we learn to code, sort of,

  • the learning never ends.

  • We push the boulder up to the top of the mountain

  • and then we think we've got it, and then we realize

  • we're just at the foot of the next mountain.

  • And so we find that our successful students

  • really enjoy the process.

  • They even enjoy, I guess it'd be generous to say

  • the mental gymnastics, it's really the mental punishment

  • of learning new things.

  • And so we need to see that students have overcome

  • difficult things in the past

  • and they've come out on the other side,

  • that they're really willing

  • to push through those challenges.

  • Culture, this is a tricky one.

  • We don't admit students, we admit classes.

  • And those classes need to be balanced of course,

  • in terms of gender and ethnic diversity.

  • But they also need to be balanced in terms of

  • background, and diversity of perspective.

  • If we have a bunch of finance folks who pass

  • all of our criteria, we may ask some of them to defer

  • because they just sort of come from the same place.

  • And we've see that having a monoculture really

  • impedes learning, motivation, and ultimately collaboration.

  • I'm gonna throw this in, but it's a little anecdotal.

  • Somewhere around 80% of our top students

  • have some sort of creative outlet.

  • Whether that be professional or as a hobby.

  • And so these are poets, these are musicians,

  • these are photographers and painters.

  • And it doesn't seem to really matter what the medium is,

  • but they just seem to approach problems differently.

  • And so, this is something that we've discovered,

  • we're still working on it, but it seems to exist.

  • So here's some actual ratings,

  • these are the actual ratings of our last class.

  • Of course, without the names.

  • And so by a show of hands, what do you think

  • was the best predictor of success in terms of

  • speed to finding a job, starting salary,

  • and employer satisfaction?

  • Was it culture fit?

  • No people.

  • Was it passion?

  • Some people.

  • Was it technical background?

  • Two brave souls.

  • Hireability?

  • Awesome.

  • And aptitude?

  • Cool.

  • It was in fact passion.

  • And it was by a large margin.

  • Passion was the best predictor of

  • whether a candidate was going to succeed.

  • And it wasn't even close.

  • What didn't matter?

  • Technical background, technical experience

  • had almost no correlation with how fast a student was hired,

  • or what their starting salary was

  • or how fast they were promoted.

  • Passionate students eventually caught up

  • with the experienced ones and then even surpassed them.

  • And so it would be great if this rubric

  • sort of worked for everyone.

  • But unfortunately, we aren't all operating

  • within the same context.

  • And so you need something that's tailored to your situation.

  • And so what's important to you?

  • What are you thinking about?

  • And there are some places that you might look

  • to determine your tools.

  • Performance reviews, although those can really be

  • a minefield if you guys aren't doing them right.

  • Mission statements, value statements,

  • sometimes those are updated, sometimes they aren't.

  • One on ones and retros, what's your team

  • actually thinking about and talking about?

  • And so, ultimately you can't be successful

  • in evaluating talent without knowing

  • what you value and what's important to you.

  • Okay.

  • Everything we've been talking about

  • has been pretty abstract.

  • Let's discuss three things that will help you

  • better think about talent, starting today and maybe

  • you'll discover some of those diamonds in the rough.

  • Admit we have no idea where talent comes from.

  • Control for sample size bias.

  • And rethink cultural fit.

  • Okay, so Trevor Hoffman was drafted as a shortstop,

  • he's converted to this pitcher.

  • Is it outside the realm of possibility that you might

  • come across someone who was in a generalist role,

  • and if they could just focus on the one thing

  • they did particularly well, they could excel.

  • What about Albert Pujols?

  • Albert Pujols got drafted

  • from Maple Woods Community College.

  • If you got a resume from Maple Woods Community College

  • how would you feel about that?

  • Is that something that you would give a lot of attention to?

  • Maybe the physical injury analogy doesn't really work here

  • as in David Ortiz's case, but what if someone had

  • a number of personal circumstances that

  • were affecting their performance.

  • What if they were going through a divorce?

  • Or what if they had a three month old who was conducting

  • some sort of twisted, sleep-deprivation experiment on them.

  • This is my guy, he is really messing with me.

  • (laughter) But I do miss him.

  • Anyway, so we're so bad at separating context

  • from personal attribution that there's a term for it,

  • it's called Fundamental Attribution Error.

  • And so, baseball is really bad at this too.

  • There's 600 players playing in the Major Leagues at one time

  • and 72 of those players play in the All-Star game.

  • So that's the top 12%.

  • Only a third of those are the slam dunk, first round

  • prospects that everyone saw coming.

  • And so, in reality, if a prospect,

  • whether that be in baseball or in software,

  • is projected to be a star, guess who else knows?

  • Everyone.

  • And if you're Google and if you're Facebook,

  • and if you're Amazon, you can afford to throw a ton of money

  • at everyone and hope that they develop.

  • But for the rest of us that need to live in reality,

  • we need to be looking for candidates

  • that fall through the cracks.

  • And they may fall through the cracks for a number of reasons

  • but ultimately they've been viewed through

  • the conventional lens where we judge their talent,

  • and that might emphasize their weaknesses

  • and obscure their strengths.

  • And so, this is really what Moneyball boils down to.

  • Is looking where others aren't.

  • The second thing is sample size bias.

  • So we're wired to make snap judgements.

  • It's no matter how thoughtful or kind we think we might be

  • we still do it.

  • And our goal is to create as many touch points as possible

  • without necessarily dedicating more time.

  • It took baseball a really long time to

  • come to the realization that one game was just a snapshot.

  • And because the software industry

  • is much, much younger by comparison,

  • I think we're still conceited enough that we can, we think

  • that we can judge someone's talent based on one interview.

  • But let's admit that we're worse at this

  • than we actually think we are.

  • So creating a fuller picture of the process, creating

  • a fuller picture is an essential part of the process.

  • This is how we currently do it, we got whiteboard challenges

  • we've got phone screens, we have open source commits,

  • we have looking at their blogs and resumes.

  • I personally like looking at blogs

  • and open source commits and any sort of evidence

  • that demonstrates consistent, long-term behavior.

  • Pairing has also been mentioned

  • as one of the better methods,

  • since it actually simulates the job that someone

  • might be looking to get, or looking to work in.

  • But ultimately, these are all data points,

  • and they need to be treated as such.

  • And in my opinion, what we're really after is,

  • how much does she love the work?

  • Is he creative?

  • And will she make your team better?

  • Traditional interview screens can do this

  • we just have to be asking the right questions.

  • As can whiteboard challenges

  • or any other data points that we use.

  • As long as we're taking the macro view as opposed to

  • this sort of one game snapshot perspective.

  • Cultural fit.

  • Cultural fit is hard.

  • And I think it's become a little bit of a mess.

  • What we're starting to screen for is much less of a match

  • with organizational values.

  • And much more of a match for personal fit.

  • And so, I think it's natural to bond over shared experiences

  • and pedigrees, and if you went to the same college

  • you can play the name game,

  • and that helps us feel connected.

  • But, cultural fit has become this new form

  • of discrimination all in the name of

  • employee enjoyment and fun.

  • And so, what are we actually looking for here?

  • Are we looking for someone that we want to have a beer with?

  • Or are we looking for someone that we want to work with?

  • To turn this around, we're gonna need some serious

  • self-reflection because the research says

  • that more diverse teams outperform less diverse ones.

  • For jobs involving complex decisions and creativity,

  • more diverse teams outperform less diverse ones.

  • We're more confident in homogenous groups

  • because that makes us feel comfortable.

  • But they just don't perform as well.

  • And so to make this happen we need to

  • confront our biases head on.

  • It's scary, it's really scary to admit

  • that we might have personal biases or institutional biases

  • but the data is likely there if you have the courage

  • to take a look at it.

  • We're comfortable with what we know,

  • for what we've seen in the past.

  • Faces like our, backgrounds like ours, skills like ours.

  • But is comfort really enough?

  • Is that good enough criteria?

  • This is Jackie Robinson signing his first contract

  • to become the first black player in the Major Leagues.

  • Now Jackie was signed by the Dodgers

  • for one reason, and one reason only.

  • And that was to win.

  • He of course went on to have a great career,

  • a hall of fame career.

  • But from a hiring perspective, the Dodgers got access

  • to a talent pipeline that no one else was willing

  • to take advantage of.

  • And so, yes it was the right thing to do from a

  • social consciousness perspective,

  • but it was also a huge competitive advantage.

  • Hiring people from non-traditional backgrounds

  • takes a lot of guts, but it's one way to take advantage

  • of a developer hiring market inefficiency.

  • These players offered incredible value to

  • the organizations that took chances on them.

  • And yes, high priced folks might be worth your resources,

  • but you just can't afford all the most expensive players

  • all the time, unless you are the New York Yankees

  • or Boston Red Sox.

  • And so, talent needs an opportunity from you.

  • Being more objective by writing things down,

  • establishing rubrics for rating guidelines,

  • introspecting on our own scouting abilities,

  • not letting cultural screens be prohibitive.

  • Just generally getting away from going with our gut

  • means that we're gonna be a more inclusive industry

  • and ultimately, push us more

  • towards a more complete meritocracy.

  • Thanks.

  • (applause)

  • How else can we look at technical background?

  • Yeah so actually, my team, we have a different

  • set of tools, I knew this was gonna come up.

  • It is communication, ownership, adaptability,

  • velocity, and quality.

  • And so, with some, given the small team that we have

  • and the speed that we're moving those are the things

  • that we look for.

  • We have our own rubrics,

  • I do grade people out when I interview them.

  • But, this is what's worked for the school

  • and again, it's really based on your context.

  • Yeah, I don't, no, no, I don't.

  • I think, I think we should.

  • I think it's sort of built in because a lot of,

  • we do hire quite a bit out of our own school too

  • because we, they get to go through the program

  • and then we know who awesome developers are

  • because they just graduated.

  • But, in terms of hiring from the outside

  • yeah, I think that's a reasonable way to approach it

  • and it's not something that I currently do.

  • Sure, so the question was, when evaluating culture fit,

  • how different is this person

  • than the rest of the organization, yeah?

  • Yeah, I mean, I think we just have to be really careful

  • of making culture fit like this prohibitive screen.

  • There's, I mean, we use Greenhouse as our software

  • within the school and you know,

  • the culture screen is one interview.

  • And we also do this sort of gladiator style, like,

  • yay or nay thing which I think works out pretty well.

  • But, it's really up to the person doing the screen.

  • Often times that's our recruiting manager.

  • For my team in particular, I think I have, it's up to me

  • to make those calls.

  • But, when I'm looking to round out my team

  • a lot of it is thinking about what we don't have.

  • So, you know, is this a person that

  • is gonna make our team better?

  • Are they creative?

  • How much do they love this?

  • And how much are they going to develop?

  • Going back to this sort of present versus future,

  • like passion just drives everything, and so

  • if that's the case and they really want to get better,

  • they're gonna get better.

  • Yeah, it's somewhere in between, right?

  • You know we, I was a student in the first class at Flatiron

  • and they didn't really know if it was going to work.

  • It was sort of like just 20 people in a loft

  • and the founder just telling us stuff.

  • It worked for me, so that works.

  • But now we have an actual system

  • and we're using the product that I'm working on

  • to sort of scale that out and we've seen things like

  • before we had the system in place versus after.

  • They can consume like twice the amount of material

  • and so we're able to cover some things, like,

  • that we just didn't cover before and so we give them

  • much more of an education but.

  • Yeah, I think it's buyer beware, right?

  • Bootcamps are a thing, they weren't really

  • when I was going through the process.

  • But they have changed a lot of people's lives,

  • so that seems to be good.

  • But, people that are getting oversold, like,

  • did they do enough research or could they not get in to

  • a more prestigious program, it's hard.

  • Yeah, we have about as long term as me.

  • I was in that first class, so yeah,

  • we're up to a little over three years.

  • (audience member asking a question)

  • We do.

  • We're really proud to have an audited jobs report

  • where we go to every single student,

  • and this is a third party consulting party

  • that we pull in and actually they contact every student,

  • talk about their salary, talk about their prospects.

  • As far as I understand, we're the only school

  • that's doing that so.

  • Yes, we're taking that data very seriously.

  • (audience member asking question)

  • Oh, in terms of the grading?

  • (audience member asking question)

  • Yeah, I don't know if that's true,

  • but given my answer to the previous question

  • is that I use a different rubric for hiring on my team.

  • I think it really is context dependent and so

  • if you're talking, if we're putting someone at Intel,

  • well they're gonna clearly have a different hiring standard

  • than we would for screening our students.

  • Thank you everyone, I really appreciate you being here.

  • (applause)

  • (upbeat country music)

(upbeat country music)

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RubyConf 2015 - キーボードでマネーボール。才能ある開発者をスカウトする方法のレッスン (RubyConf 2015 - Moneyball at the keyboard: Lessons on how to Scout Talented Developers)

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    Pedroli Li に公開 2021 年 01 月 14 日
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