- Kevin is a partner at Y Combinator and founded a company called Wufoo
- Jake is the founder and CEO of Insight, an education company that helps scientists and engineers transition to careers in data science and AI
- Insight runs free fellowships where participants build projects for 7 weeks and get hired by top data teams
- Jake applied to Y Combinator in 2011 and started Insight to solve the problem of transitioning into data science
- The first session of Insight was a success, with all 8 students getting jobs at top data teams
- Jake qualified applicants based on their excitement and curiosity about the field
- Companies were interested in candidates with side projects and a strong sense of curiosity

- The speaker discusses the impact of data and the problem-solving potential it holds.
- They mention the process of selecting the right candidates for their program, highlighting the importance of finding individuals with diverse skills and a willingness to learn.
- The speaker emphasizes the need for data scientists to have strong communication skills and an understanding of the business and product problems they are trying to solve.
- They explain the different types of data science roles, including product analytics, data product, and AI/machine learning engineering roles.
- The speaker advises data scientists to be cautious when choosing a company to work for, ensuring that the company has a clear mission and a genuine need for data analysis.
- They suggest that startups consider hiring a data science advisor before bringing in a full-time data scientist to help with data strategy and infrastructure.
- The video ends by mentioning the initial class size of eight students in the speaker's program.

- The program has expanded to 5 cities and offers specializations in data science, data engineering, health data, AI, and product management.
- Each program has a maximum of 20 to 35 fellows to encourage collaboration and teamwork.
- The program lasts for 7 weeks and focuses on fast-paced learning and project development.
- Projects should be focused on solving real-world problems and creating useful products.
- The program aims to teach fellows about product development and the importance of understanding user needs.
- Data cleaning is a significant part of the job for data scientists, and fellows are taught how to handle and analyze messy data.

- Founders often assume they are tracking all the necessary data, but often overlook important details such as user logins.
- There are no easy answers or ready-made tools for tracking data, especially for startups building specific products.
- Startups should focus on optimizing 1 or 2 key metrics that align with their goals.
- Churn prediction models and experimentation are important for reducing churn and increasing conversion.
- Understanding user behavior and building to their needs is crucial for success.
- Cohort analysis and retention curves help identify churn problems and improve the product.
- Open-source tools like Python are widely used in the data science field.
- Data scientists come from various backgrounds and each brings unique strengths to the table.

- The speaker discusses the importance of networking and collaboration in fields like psychology and neuroscience.
- Startups that recruit data scientists successfully emphasize the impact that these scientists can have on the success of the company.
- Contracting can be useful for prototyping and testing new algorithms or technologies.
- The speaker is particularly excited about the impact of data science in the field of health, citing examples of early cancer detection and personalized clinical trial recommendations.
- Data science is expanding beyond business optimization to create value in various industries, including healthcare.

- Kevin is a partner at Y Combinator and founded Wufoo
- Jake is the founder and CEO of Insight, an education company helping scientists transition to data science careers
- Insight offers free fellowships where participants build projects for 7 weeks and get hired by top data teams
- Jake started Insight to solve the problem of transitioning into data science and the first session was successful
- Candidates are selected based on their curiosity and excitement about the field, as well as side projects and curiosity
- Data scientists need strong communication skills and understanding of business and product problems
- Different types of data science roles include product analytics, data product, and AI/machine learning engineering
- Startups should have a clear mission and genuine need for data analysis before hiring data scientists
- The program has expanded to 5 cities and offers specializations in various areas of data science
- Fellows learn about product development, handling messy data, and optimizing key metrics
- Open-source tools like Python are widely used in data science
- Networking and collaboration are important in fields like psychology and neuroscience
- Data science has potential impact in healthcare, including early cancer detection and personalized clinical trial recommendations.

Jake Klamka founded Insight. Insight provides intensive 7 week professional training fellowships in fields such as data science and data engineering. Insight was in the YC Winter 2011 batch.

https://twitter.com/jakeklamka
https://www.insightdatascience.com/

Kevin Hale is a Visiting Partner at YC. Before YC Kevin was the cofounder of Wufoo, which was funded by YC in 2006 and acquired by SurveyMonkey in 2011.

https://twitter.com/ilikevests

The YC podcast is hosted by Craig Cannon - https://twitter.com/craigcannon

***

Topics

00:00 - Kevin's intro

00:30 - Jake's intro

1:05 - Applying to YC with one product then changing it

3:30 - How Insight started

4:20 - Jake's first students and initial coursework

8:00 - Finding out what companies want from data scientists

10:00 - Picking the first class of students

11:30 - Common pitfalls for people transitioning into data science

14:30 - Types of data science roles

16:45 - What data scientists should look out for in companies

17:40 - Chuck Grimmett asks - When do you know you need to bring in seasoned data scientists?

20:00 - How Insight has scaled and changed

22:00 - What happens in the program

23:20 - Examples of a good project for a data science resume

25:50 - Will more data scientists be founders in the future?

28:00 - Teaching product

29:00 - Cleaning data

31:30 - Tools for tracking data

32:30 - Track what are you trying to optimize

35:20 - Churn and conversion

39:00 - Is there an ideal background for a data scientist?

41:00 - Which startups recruit well at Insight?

43:00 - Contracting

45:40 - Fields Jake is excited about

So Kevin, for those of our listeners that don't know who you are, what's your deal?

I was in winter 2006, and I founded a company called Wufoo.

Ran that for 5 years, and then We were acquired by SurveyMonkey, and that moved us from Florida to California.

And that's when PG asked if I'd be interested in helping out at YC.

Yeah, and you suggested Jake as a guest for this episode.

Jake Leach So I'm the founder and CEO of Insight.

We run fellows programs that help scientists and engineers transition to careers in data science and AI.

It's a pretty unique model because they're completely free, these fellowships.

Engineers, scientists, build projects for 7 weeks.

They meet top data teams, and they get hired on those teams.

We've got over 2,000 Insight alumni working as data scientists now across the US and Canada.

And you always haven't been working on this.

So you applied to YC for the winter 2011 batch.

So I was back in, so I started my career, and this is relevant to why I started Insight, because I basically started, I wish that existed.

When I was around, I was a physicist at the University of Toronto.

I thought I was going to be a scientist for the rest of my life.

And then partway through my PhD I realized I want to go into technology.

And I think to myself, I'm writing code, I'm building machine learning models.

And it frankly took me a long time to transition.

Eventually got into Y Combinator, came down here from the Winter 11 batch.

I was building a bunch of time mobile productivity apps that were machine learning enabled.

And didn't quite get the up and to the right graph that you would hope for after YC.

And in that sort of late 2011, after 6, 12 months after YC, was searching for a new idea.

And actually spoke with Paul Graham and a few other advisors, And the recommendation was work on a problem you yourself have.

You're kind of building these apps that you're trying to use these machine learning models.

And hopefully somebody's got that as a problem.

Start with a problem you've had, then figure out what the solution is.

And when I reflected on it, it took me a few years to really make this transition.

I'd been so close all along, but I didn't know product.

There's a bunch of, you know, technically I had the fundamentals, but a lot of the tool sets were different in the industry.

And when I got down here and I started talking to people, that's when I finally started figuring it out.

And was seeing a lot of my friends having that same struggle.

So brilliant mathematicians, neuroscientists, biologists, also engineers later we found the same thing.

Kind of getting stuck and like they're like, I want to go into data science, I want to go into AI, I want to go into these cutting edge fields, but it doesn't say the right thing on my resume, or I'm just getting that last mile is really hard to cross.

I thought, this is a problem I want to solve.

Because these are some of the most brilliant people I'd ever worked with.

A lot of them were my former colleagues from physics.

And I thought, what does the solution for this look like?

And at first I was focused on it's going to be an app again, right?

And then I realized, no, it actually probably looks more like an in-person program where folks are getting together, building cool projects, and then getting started from there.

And so did you just go ahead and teach a class?

First I talked to companies And I said, listen, I've got these brilliant friends coming out of academia who I think you should be hiring.

And basically what they told me is, I know they're brilliant.

I know they've got all these great skills.

But they're probably like 1 to 2 months away from where I need them to be in terms of if I had full days to mentor them for a month or 2, they'd be an incredible data scientist.

But they're like, I don't have a month or 2 to mentor them.

And so I'm like, well, I have a month or 2.

So maybe what Insight is going to be is that month or 2 where folks are filling in those last pieces of the puzzle, learning the cutting edge techniques and tool sets and other things.

And then let's bring those data scientists in the room and have them hire.

And then we just jumped right in and I ran the first sessions.

So At first I started talking to my friends in academia.

I got confirmation from my friends in academia that, I already knew that they were looking for jobs and they were excited about transitioning.

I got confirmation from the hiring managers to say, listen, we're hiring, we can't find folks with the full skill set, if you bring them into a room, we'll go look at them." And then the rest was getting the word out.

Because you mentioned that you didn't know what you didn't know.

By that time I had spent 3 years figuring it out, including doing YC and meeting a bunch of data scientists and building a bunch of data products.

So by that point I kind of knew what the pieces were.

But also really the program was focused not on me teaching the fellows, it was focused on me bringing in the leading data scientists at the time and having them directly tell them.

So we had Facebook, LinkedIn, Twitter, Square, all these early data teams in 2012, their heads of data science come in.

So they're willing to do 1 day, they just couldn't commit a month.

They're like, I'll come in for a few hours, but I don't have 2 months.

And I'm like, well, if I have a bunch of you come in for a few hours, plus really have these folks kind of working away for a few months, learning from each other, learning from these mentors.

Once we had alumni too, it was incredible.

We had all these alumni coming in to help.

And then how many of them did you get them jobs?

Yeah, 1 went to Facebook, 1 went to Square, 1 went to LinkedIn, 1 went to Twitter.

I mean at the time these were like, they still are the top data teams, but I mean it was a clear success.

I didn't have the model, I hadn't figured it out.

Was that first class kind of a shit show?

In the sense that It was the first time I was doing it.

And a lot of it, a career transition is always stressful.

Whenever people are doing Insight, they're stressed.

But at least there's a track record there and now we have things bake pretty well.

At that time, the overall idea was there, but a lot of the details weren't there.

And frankly, the track record wasn't there.

So a lot of these folks are like, what have I done?

I'm in a room with this guy who's never done this before.

So there's a lot of stress just around, is this even going to work, this weird model?

Part of the application process, I got way more applications than I expected when I started it.

There was a real demand to get into the field.

I didn't have a track record, but I basically went around to these universities and said, I'm going to have the head of data science from Facebook, LinkedIn, Twitter, all these companies coming in, and you're going to meet them.

And my interview process really centered around how excited you were about this.

So the folks who were like, I really don't want to do this, but I need a plan B, no thank you.

It was the people who said to me, I love my work as a scientist, but I really want to have more of an applied impact in the world, I'm excited about what I'm seeing here, here's what I think I can do.

I mean, that's the kind of folks I would take into the program.

It's such a more common technique you're seeing a lot of startups do now, like Superhuman, for example.

Just heavily qualifying a lead before they'll even let them access to the product.

So that way you're trying to guarantee that the time I do spend

with someone that's going to have a spectacular experience.

That's why these hiring managers wanted to come in.

How did you figure out what students were going to be the most excited about this?

I had some opinions, but really what I did is I went to these early heads of data science teams and said, what do you look for?

And what they said is, they list off some technical skills, but it's kind of like laser, they need to know SQL, they need to know Python, they need to know, and I'm like, okay, but What really would clinch it for you?

There's if they have a side project, and their eyes would light up.

They'd go, oh, if they had a side project, and if they send me a URL, oh, then I know they're excited.

And so that's where the idea came around for, hey, this isn't about, you know, these folks have been through enough classes.

It's about actually building, it's about actually creating something and proving that I've got all these great background, but now I'm gonna do this last piece of the puzzle to show you I can do something relevant in this area.

And the second thing that they wanted, and I think this is where the project really shows this, but they wanted overall is just curiosity.

So folks, and I thought that they weren't being serious, to be honest with you, because I was like, yeah, you say you want curiosity, but really you just want somebody who's good at like SQL or something, right?

The people they were higher would be the ones who were the people who, hey, I studied astrophysics, but in my spare time I was like, I'm dabbling with genomics.

And then I got into machine learning on the side.

And then I built this cool for fun project that predicts where I should go to camping or something, because I'm a big camper or something.

And then you take a person like that, and that's the kind of folks that these teams want and still want, because these problems are so open-ended.

it's like curious people don't get blocked as

The roles our fellows are getting hired into, most of the companies, it's not like we know what we need you to do, just do it.

What kind of impact can we have with data?

Like, the project seems like, OK, that's something we have to shoot for.

But again, it's like, how did you know that these were the right 8 people?

Well, so a lot of it was trial and error.

I would do like 12 plus interviews a day.

And you kind of get to know folks and kind of get to know it.

But I think the main thing, the signal that I saw was, it's kind of that example I gave, is that almost people would be almost apologetic.

They'd be like, listen, what I'm about to tell you is not part of my usual work, but it's on the side.

And it's like, no, no, no, I want to hear about that.

I remember I had this 1 of the fellows came, well, she became a fellow, but she was in an early session, she was a mathematician at Berkeley.

And she had done all this incredible analysis.

Like this really cool data analysis project, I think on like, maybe flight times or something in sports.

And partway through the interview, I'm like, but you're a mathematician.

I had done some math, she's like, I can't remember what the field was.

And she's like, oh yeah, this is not even part of my, and she almost felt kind of apologetic, but I was like, this is who I want as a fellow.

Brilliant mathematician, doing incredible work, and able to, on the side, on the weekend, quickly pick up Python, this, that, the other, make something useful.

She went to Facebook after the program, been super successful ever since.

So It's people like that that you're like, I want you.

So this is related to 1 of these overarching questions we had for you.

So basically, it's like, how can people get into data science?

And then what are the pitfalls for people who, say, have a PhD, they know Python, they're at a higher level than a coding boot camp person.

What are the pitfalls they make when they're trying to bridge that gap and get into a data science role, provided that they didn't do your program?

I started with scientists, and now we also have programs for engineers who are transitioning to machine learning, engineering, deep learning research.

And you sort of see very similar problem on both sides, which is folks are extremely focused on the sort of technical, let me get the algorithmic knowledge down, let me know every last algorithm, which of course you need and you need those foundations.

But when you're already dealing with someone who has been doing a bunch of work for years in a PhD or in engineering in these areas, what you actually want to see and what these teams want to see is communication ability.

Its ability to understand the underlying business and product problem.

Because what they want to do is hire someone who's going to first think about what are we trying to accomplish here?

And then figure out how do I use my tool set of machine learning or analysis to do it.

And what often happens, and this is the pitfall, is part of why you get into it is because you're excited about that analysis, excited about the machine learning.

And so you start always putting that first.

And you're always like, let me tell you the algorithms I can build.

And it's like, what folks need to start, who are trying to transition into it, need to start thinking about product, need to start thinking about business, need

what are they actually trying to achieve?

And what's interesting about the advice that we give to a lot of people about sales is it's not about selling your own thing, it's about understanding their problem.

And then feeding whatever you have to them.

And so it seems like for the data science, the same thing needs to happen.

It's not saying, Here's all the things I have.

It is like, try to figure out what it is that you fit into for them.

And it's like, understand the underlying, forget data, forget machine learning algorithms.

Like making yourself look like the solution.

Not trying to be like, oh, I have a bunch of stuff.

Which 1 of these things are you interested in?

I have a hammer and a screwdriver, which I can use all of them.

It's like, what are we trying to build here?

And sometimes that's actually a separate role.

So for instance, say like Facebook might list a data science job.

Whereas some smaller startup would say like, we have an engineering role open.

And you might classify yourself as a data scientist.

you have to pitch data science to a startup, how do you do that as an engineer?

So first of all, data science, machine learning, these are all super broad umbrella terms.

Ryan Seilhamer Maybe you should define data science.

Peter Miller Maybe what I'll do is define data science, and I think this is essentially answering your question.

So what we see in the industry, kind of broadly speaking, broad terms, details, let's not worry about the details, is sort of, I see kind of 3 big pieces of how sort of data science is used.

So some data science roles are what I kind of call product analytics or business analytics roles.

The idea there is you're looking for a better understanding.

You're analyzing data about users or a company and trying to understand how to improve it, help users succeed, help the business succeed.

The second types of roles that we see are data product roles.

So these are roles where you're actually using machine learning and predictive models to actually change the user experience and give them something they want right there and then as part of the product.

And the third 1 is usually what you hear termed as AI, which is AI roles, machine learning engineering roles, where it's not just a feature in the product, that's the prediction, it's the product, it's machine learning.

If the machine learning doesn't work, the whole product doesn't work.

And so you'll have an example of a lot of teams misunderstanding which 1 they need.

Is there not a category in between where it's like, oh, machine learning supplements a feature or augments?

Yeah, so usually that's where folks talk about data products.

When they talk about data products, it's often a feature.

That's a situation where, honestly, if they didn't have machine learning, they could still just say, here are the top movies, go watch them.

But with that predictive model, you're really getting a much better experience.

We have probably 30 plus fellows working at Netflix.

But some of them work on analytics, which is, how are people even using this product?

What can we add a more product level do to improve it?

And there the output isn't a feature that the user sees, like an actual algorithm serving recommendations.

There it's like they have to go and communicate with the product team to say, hey, users seem to want us to be building this sort of product for them.

Let's over the next 6 to 12 months take the product in that direction.

Edith Leonard So I know what the dream scenario for a lot of data scientists are.

I want to get a job working on these interesting problems.

What should they look out for that they should avoid in a company?

Because I think everyone's kind of thinking, or more than should, is looking for a data scientist.

should a data scientist be worried about?

It's like, oh, they're not ready to actually hire me.

And if I go here, this will be a bad experience.

Remember how I said the data scientist needs to know what the actual problem is?

The company needs to know what the actual problem is.

And so the companies you need to be wary about are the ones where it's like, hey, you know what, I want deep learning.

What do you want us to do here and why do you need it?

And the company you want to go to is the 1 that's got a mission you align with.

You want to have whatever solution they're bringing to the market thrive in the world.

And then they have a clear sense of, if we add some data analysis to this, if we add machine learning to this, it's going to be better.

Chuck Grimitt asked, when do you know you need to bring in seasoned data scientists?

Is there any kind of benchmark you can offer?

Yeah, I think, so first of all, I think you have to start as a founder, start with the idea, and you can do this, I recommend this, before you have a data scientist, understand, is data sort of critical to building my product, Or is it something that I'll just add on once it's already working and I need to optimize the experience?

An example for something critical is Amazon Alexa.

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Transitioning from Academia to Data Science - Jake Klamka with Kevin Hale