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Sold to mayuresh.shilotri@gmail.com T H E DATA SCIENCE HANDBOOK A D V I C E A N D I N S I G H T S F R O M 25 AMAZING DATA SCIENTISTS F O R E W O R D B Y J A K E K L A M K A DJ Patil, Hilary Mason, Pete Skomoroch, Riley Newman, Jonathan Goldman, Michael Hochster, George Roumeliotis, Kevin Novak, Jace Kohlmeier, Chris Moody, Erich Owens, Luis Sanchez, Eithon Cadag, Sean Gourley, Clare Corthell, Diane Wu, Joe Blitzstein, Josh Wills, Bradley Voytek, Michelangelo D’Agostino, Mike Dewar, Kunal Punera, William Chen, John Foreman, Drew Conway B Y C A R L S H A N H E N R Y W A N G W I L L I A M C H E N M A X S O N G
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CONTENTS Preface by Jake Klamka, Insight Data Science 1 Introduction 4 Chapter 1: DJ Patil, VP of Product at RelateIQ The Importance of Taking Chances and Giving Back 6 Chapter 2: Hilary Mason, Founder at Fast Forward Labs On Becoming a Successful Data Scientist 17 Chapter 3: Pete Skomoroch, Data Scientist at Data Wrangling Software is Eating the World, and It’s Replacing it With Data 27 Chapter 4: Mike Dewar, Data Scientist at New York Times Data Science in Journalism 40 Chapter 5: Riley Newman, Head of Data at AirBnB Data Is The Voice Of Your Customer 49 Chapter 6: Clare Corthell, Data Scientist at Mattermark Creating Your Own Data Science Curriculum 56 Chapter 7: Drew Conway, Head of Data at Project Florida Human Problems Won’t Be Solved by Root-Mean-Squared Error 64 Chapter 8: Kevin Novak, Head of Data Science at Uber Data Science: Software Carpentry, Engineering and Product 76 Chapter 9: Chris Moody, Data Scientist at Square From Astrophysics to Data Science 84
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CONTENTS Chapter 10: Erich Owens, Data Engineer at Facebook The Importance of Software Engineering in Data Science 95 Chapter 11: Eithon Cadag, Principal Data Scientist at Ayasdi Bridging the Chasm: From Bioinformatics to Data Science 102 Chapter 12: George Roumeliotis, Senior Data Scientist at Intuit How to Develop Data Science Skills 115 Chapter 13: Diane Wu, Data Scientist at Palantir The Interplay Between Science, Engineering and Data Science 123 Chapter 14: Jace Kohlmeier, Dean of Data Science at Khan Academy From High Frequency Trading to Powering Personalized Education 130 Chapter 15: Joe Blitzstein, Professor of Statistics at Harvard University Teaching Data Science and Storytelling 140 Chapter 16: John Foreman, Chief Data Scientist at MailChimp Data Science is not a Kaggle Competition 151 Chapter 17: Josh Wills, Director of Data Science at Cloudera Mathematics, Ego Death and Becoming a Better Programmer 169 Chapter 18: Bradley Voytek, Computational Cognitive Science Professor at UCSD Data Science, Zombies and Academia 181 Chapter 19: Luis Sanchez, Founder and Data Scientist at ttwick Academia, Quantitative Finance and Entrepreneurship 191 Chapter 20:Michelangelo D’Agostino,Lead Data Scientist at Civis Analytics The U.S. Presidential Elections as a Physical Science 202
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CONTENTS Chapter 21: Michael Hochster, Director of Data Science at LinkedIn The Importance of Developing Data Sense 213 Chapter 22: Kunal Punera, Co-Founder/CTO at Bento Labs Data Mining, Data Products, and Entrepreneurship 227 Chapter 23: Sean Gourley, Co-founder and CTO at Quid From Modeling War to Augmenting Human Intelligence 245 Chapter 24: Jonathan Goldman, Dir. of Data Science & Analytics at Intuit How to Build Novel Data Products and Companies 266 Chapter 25: William Chen, Data Scientist at Quora From Undergraduate to Data Science 272 About the Authors 279
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PREFACE In the past five years, data science has made an impact in almost every major area of human endeavour. From commerce, to education, to energy, and of course, software and the Internet, data science has created immense value across the world. In fact, in early 2015 the President of the United States announced the new role of Chief Data Scientist to the White House and appointed DJ Patil, one of the interviewees in this book, to the position. Like many innovations in the world, the birth of this industry was started by a few motivated people. Over the last few years, they founded, developed and advocated for the value that data analytics can bring to every industry. In The Data Science Handbook, you will have the opportunity to meet many of these founding data scientists, hear first hand accounts of the incredible journeys they took, and read where they believe the field is headed. The road to becoming a data scientist is not always an easy one. When I tried to transition from experimental particle physics to industry, resources were few and far between. In fact, although a need for data science existed in companies, the job title had not even been created. I spent a lot of time teaching myself, working on various startup projects, and later saw many of my friends from academia run into the same challenges. I observed a groundswell of incredibly gifted and highly trained researchers who were excited about moving into data-driven roles, yet were missing key pieces of knowledge about how to do so. As a result, they had trouble transferring their incredible quantitative and data analysis research skills to a career in industry. Meanwhile, having lived and worked in Silicon Valley, I also saw that there was very strong demand from technology companies who wanted to hire these exact people. To help others bridge the gap between academia and industry, I founded the Insight Data Science Fellows Program in 2012. Insight is a training fellowship that helps quantitative PhDs transition from academia to industry.Over the last few years,we’ve helped hundreds of Insight Fellows, from fields like physics, computational biology, neuroscience, math, and engineering, transition from a background in academia to become leading data scientists at companies like Facebook, Airbnb, LinkedIn, The New York Times, Memorial Sloan Kettering Cancer Center and nearly a hundred other companies. In my personal journey to both entering the technology field as well as creating a community for others to do the same, one key resource I found to be tremendously useful was conversations with those who had successfully made the transition. As I developed Insight, I have had the chance to engage with some of Silicon Valley’s best data scientists who are mentors to the program:
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PREFACE 2 Jonathan Goldman created one of the first data products at LinkedIn—People You May Know—which transformed the growth trajectory of the company. DJ Patil built and grew the data science team at LinkedIn into a powerhouse and co-coined the term “Data Scientist.” Riley Newman worked on developing product analytics that was instrumental in Airbnb’s growth. Jace Kohlmeier led the data team at Khan Academy that helped optimize learning for millions of students. Unfortunately, it’s hard to get face-to-face time with these remarkable people. At Insight, to maintain an exceptionally high quality and personal time with these mentors, we select only a small group of talented scientists and engineers three times per year. However, The Data Science Handbook provides readers with a way to have these in- depth conversation at scale. By reading the interviews in The Data Science Handbook, you will have the experience of learning from the leaders in data science at your own pace, no matter where you are in the world. Each interview is an in-depth conversation, covering the personal stories of these data scientists from their initial experiences that helped them find their own path to a career in data science. It’s not just the early data science leaders who can have a big impact on the field. There is also new talent entering, with the opportunity for each and every new member to push the field forward. When I met the authors of this book, they were still college students and aspiring data scientists, full of the same questions that those beginning in data science have. Through 18 months of hard work, they have done the legwork in seeking out some of the best data scientists around the country, and asking them for their advice and guidance. This book is the result of that work, containing over 100 hours of collected wisdom with people otherwise inaccessible to most of us (imagine having to compete with President Obama to talk with DJ Patil!). By reading these extended, informal interviews, you will get to sit down with industry trailblazers like DJ Patil, Jonathan Goldman and Pete Skomoroch, who were all part of the early, core LinkedIn data science teams. You will meet with Hilary Mason and Drew Conway, who were instrumental in creating the thriving New York data science community. You will hear advice from the next generation of data science leaders, like Diane Wu and Chris Moody, both Insight Alumni, who are now blazing new trails at MetaMinds and Stitch Fix. You will meet data scientists who are having a big impact in academia, including Bradley Voytek from UC San Diego and Joe Blitzstein from Harvard. You will meet data scientists
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PREFACE 3 in startups, such as Clare Corthell from Mattermark and Kunal Punera of Bento Labs, who will share how they use data science as a core competitive advantage. The data scientists in the Data Science Handbook, along with dozens of others, have helped create the very industry that is now having such a tremendous impact on the world. Here in this book, they discuss the mindset that allowed them to create this industry, address misconceptions about the field, share stories of specific challenges and victories, and talk about what they look for when building their teams. I hope that by reading their stories, hearing how they think, and learning their vision for the future of data science, you will come to think of ways you can have an impact, and perhaps even advance the field yourself. Jake Klamka Founder Insight Data Science Fellows Program Insight Data Engineering Fellows Program Insight Health Data Science Fellows Program
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INTRODUCTION Welcome to The Data Science Handbook! In the following pages, you will find in-depth interviews with 25 remarkable data scientists. They hail from a wide selection of backgrounds, disciplines, and industries. Some of them, like DJ Patil and Hilary Mason, were part of the trailblazing wave of data scientists who catapulted the field into national attention. Others are at the start of their careers, such as Clare Corthell, who made her own path to data science by creating the Open Source Data Science Masters, a self-guided curriculum built on freely available internet resources. How We Hope You Can Use This Book In assembling this book, we wanted to create something that could both last the test of time as well as address your interest in data science no matter what background you may have. We crafted our book so that it can be something you come back to again and again, to re-read at different stages in your career as a data professional. Below, we’ve listed the knowledge our book can offer. While each interview is fascinating in its own right, and covers a large portion of the knowledge spectrum, we’ve highlighted a few interviews to give you a quick start: • As an aspiring data scientist - you’ll find concrete examples and advice of how to transition into the industry. • Suggested interviews: William Chen, Clare Corthell, Diane Wu • As a working data scientist- you’ll find suggestions on how to become more effective and grow in your career. • Suggested interviews: Josh Wills, Kunal Punera, Jace Kohlmeier • As a leader of a data science team - you’ll find time-tested advice on how to hire other data scientists, build a team, and work with product and engineering. • Suggested interviews: Riley Newman, John Foreman, Kevin Novak • As an entrepreneur or business owner - you’ll find insights on the future of data science and the opportunities on the horizon. • Suggested interviews: Sean Gourley, Jonathan Goldman, Luis Sanchez • As a data-curious citizen - you’ll find narratives and histories of the field, from some of the first data pioneers. • Suggested interviews: DJ Patil, Hilary Mason, Drew Conway, Pete Skomoroch In collecting, curating and editing these interviews, we focused on having a deep and stimulating conversation with each data scientist. Much of what’s inside is being told publicly for the first time. You’ll hear about their personal backgrounds, worldviews, career trajectories and life advice.
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INTRODUCTION 5 In the following pages, you’ll learn how these data scientists navigated questions such as: • Why is data science so important in today’s world and economy? • How does one master the triple disciplines of programming, statistics and domain expertise to become an effective data scientist? • How do you transition from academia, or other fields, to a position in data science? • What separates the work of a data scientists from a statistician, and a software engineer? How can they work together? • What should you look for when evaluating data science roles at companies? • What does it take to build an effective data science team? • What mindsets, techniques and skills distinguishes a great data scientist from the merely good? • What lies in the future for data science? After you read these interviews, we hope that you will see the road to becoming a data scientist is as diverse and varied as the discipline itself. Good luck on your own journey, and and feel free to get in touch with us at contact@thedatasciencehandbook.com! — Carl, Henry, William and Max
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DJ PATIL VP of Product at RelateIQ The Importance of Taking Chances and Giving Back DJ Patil is co-coiner of the term ‘Data Scientist’ and co- author of the Harvard Business Review article: “Data Scientist: Sexiest Job of the 21st Century.” Fascinated by math at an early age, DJ completed a B.A. in Mathematics at University of California, San Diego and a PhD in Applied Mathematics at University of Maryland where he studied nonlinear dynamics, chaos theory, and complexity. Before joining the tech world, he did nearly a decade of research in meteorology, and consulted for the Department of Defense and Department of Energy. During his tech career, DJ has worked at eBay as a Principal Architect and Research Scientist, and at LinkedIn as Head of Data Products, where he co-coined the term “Data Scientist” with Jeff Hammerbacher and built one of the premier data science teams. He is now VP of Product at RelateIQ, a next generation, data-driven customer relationship management (CRM) software. Most recently RelateIQ was acquired by Salesforce.com for its novel data science technology. In his interview, DJ talks about the importance of taking chances, seeking accelerations in learning, working on teams, rekindling curiosity, and giving back to the community that invests in you. Since we interviewed him, DJ has gone on to be appointed by President Barack Obama as the first United States Chief Data Scientist. Something that touched a lot of people from your presentations is your speech on failure. It’s surprising to see someone as accomplished as yourself talk about failure. Can you tell us a bit more about that? Something most people struggle with when starting their career is how they enter the job market correctly. The first role you have places you in a “box” that other people use to infer what skills you have. If you enter as a salesperson you’re into sales, if you enter as a media person you’re into media, if you enter as a product person you’re into products etc. Certain boxes make more sense to transition in or out of than other ones. The academic box is a tough one because automatically, by definition, you’re an academic. The question is: Where do you go from there? How do you jump into a different box? I think we have a challenge that people and organizations like to hire others like
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DJ PATIL 7 themselves. For example, at Ayasdi (a topological machine learning company) there’s a disproportionate amount of mathematicians and a surprising number of topologists. For most people who come from academia, the first step is that someone has to take a risk on you. Expect that you’re going to have to talk to lots and lots of people. It took me 6 months before eBay took a chance on me. Nobody just discovers you at a cafe and says “Hey, by the way you’re writing on that piece of napkin, you must be smart!” That’s not how it works, you must put yourself in positions where somebody can actually take a risk on you, before they can give you that opportunity. And to do that, you must have failed many times, Nobody just discovers you at a cafe and says “Hey, to the point where some by the way you’re writing on that piece of napkin, you people are not willing to must be smart!” That’s not how it works, you must take a risk on you. You put yourself in positions where somebody can actually don’t get your lucky break take a risk on you, before they can give you that without seeing a lot of opportunity. people slamming doors in your face. Also, it’s not like the way that you describe yourself is staying the same; your description is changing and evolving every time you talk to someone. You are doing data science in that way. You’re iterating on how you are presenting yourself and you’re trying to figure out what works. Finally someone takes a chance on you, but once you’ve found somebody, the question is how do you set yourself up for success once you get in? I think one of the great things about data science is it’s ambiguous enough now, so that a lot of people with extra training fit the mold naturally. People say, “Hey, sure you can be a data scientist! Maybe your coding isn’t software engineering quality coding, but your ability to learn about a problem and apply these other tools is fantastic.” Nobody in the company actually knows what these tools are supposed to be, so you get to figure it out. It gives you latitude. The book isn’t written yet, so it’s really exciting. What would you suggest as the first step to putting yourself out there and figuring out what one should know? How does one first demonstrate one’s value? It first starts by proving you can do something, that you can make something. I tell every graduate student to do the following exercise: when I was a grad student I went around to my whole department and said,“I want to be a mathematician. When I say the word mathematician, what does that mean to you? What must every mathematician know?”
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DJ PATIL 8 I did it, and the answers I got were all different. What the hell was I supposed to do? No one had a clear definition of what a mathematician is! But I thought, there must be some underlying basis. Of course, there’s a common denominator that many people came from. I said, okay, there seem to be about three or four different segmentations. The segmentation I thought was the most important was the segmentation that gave you the best optionality to change if it ended up being a bad idea. As a result of that, I took a lot of differential equations classes, and a bunch of probability classes, even though that wasn’t my thing. I audited classes, I knew how to code, I was learning a lot about physics — I did everything I could that was going to translate to something that I could do more broadly. Many people who come out of academia are very one-dimensional. They haven’t proven that they can make anything, all they’ve proven is that they can study something that nobody (except maybe their advisor and their advisor’s past two students) cares about. That’s a mistake in my opinion. During that time, you can solve that hard PhD caliber problem AND develop other skills. For example, aside from your time in the lab, you can be out interacting with people, going to lectures that add value, attending hackathons, learning how to build things. It’s the same reason that we don’t tell someone, “First, you have to do research and then you It first starts by proving you can learn to give a talk.” These things happen do something, that you can make together. One amplifies the other. something. So my argument is that people right now don’t know how to make things. And once you make it, you must also be able to tell the story, to create a narrative around why you made it. With that comes the other thing that most academics are not good at. They like to tell you, rather than listen to you, so they don’t actually listen to the problem. In academia, the first thing you do is sit at your desk and then close the door. There’s no door anywhere in Silicon Valley; you’re out on the open floor. These people are very much culture shocked when people tell them, “No you must be working, collaborating, engaging, fighting, debating, rather than hiding behind the desk and the door.” I think that’s just lacking in the training, and where academia fails people. They don’t get a chance to work in teams; they don’t work in groups. Undergrad education, however is undergoing some radical transformations. We’re seeing that shift if you just compare the amount of hackathons, collaboration, team projects
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DJ PATIL 9 that exist today versus a few years ago. It’s really about getting people trained and ready for the work force. The Masters students do some of that as well but the PhDs do not. I think it’s because many academics are interested in training replicas of themselves rather than doing what’s right for society and giving people the optionality as individuals to make choices. How does collaboration change from academic graduate programs to working in industry? People make a mistake by forgetting that data science is a team sport. People might People make a mistake by forgetting point to people like me or Hammerbacher or that data science is a team sport. Hilary or Peter Norvig and they say, oh look at these people! It’s false, it’s totally false, there’s not one single data scientist that does it all on their own. data science is a team sport, somebody has to bring the data together, somebody has to move it, someone needs to analyse it, someone needs to be there to bounce ideas around. Jeff couldn’t have done this without the rest of the infrastructure team at Facebook, the team he helped put together. There are dozens and dozens of people that I could not have done it without, and that’s true for everyone! Because it’s a bit like academia, people see data scientists as solo hunters. That’s a false representation, largely because of media and the way things get interpreted. Do you think there’s going to be this evolution of people in data science who work for a few years, then take those skills and then apply them to all sorts of different problem domains, like in civics, education and health care? I think it’s the beginning of a trend. I hope it becomes one. Datakind is one of the first examples of that, and so is data science for Social Good. One of the ones that’s personally close to my heart is something called Crisis Text Line. It comes out of DoSomething.org — they started this really clever texting campaign as a suicide prevention hotline and the result is we started getting these text messages that were just heart wrenching. There were calls that said “I’ve been raped by my father,” “I’m going to cut myself,” “I’m going to take pills,” really just tragic stuff. Most teens nowadays do not interact by voice - calling is tough but texting is easy. The amount of information that is going back and forth between people who need help and people who can provide help through Crisis Text Line is astonishing. How do we do it? How does it happen? There are some very clever data scientists there who are drawn to working on this because of its mission, which is to help teens in crisis.
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DJ PATIL 10 There’s a bunch of technology that is allowing us to do things that couldn’t be done five, six years ago because you’d need this big heavyweight technology that cost a lot of money. Today, you can just spin up your favorite technology stack and get going. These guys are doing phenomenal work. They are literally saving lives. The sophistication that I see from such a small organization in terms of their dashboards rivals some of the much bigger, well-funded types of places. This is because they’re good at it. They have access to the technology, they have the brain power. We have people jumping in who want to help, and we’re seeing this as not just a data science thing but as a generational thing where all technologists are willing to help each other as long as it’s for a great mission. Jennifer Aaker just wrote about this in aNew York Timesop-ed piece — that the millennial generation is much more mission driven. What defines happiness for them is the ability to help others. I think that there is a fundamental shift happening. In my generation it’s ruled by empathy. In your generation, it’s about compassion. The difference between empathy and compassion is big. Empathy is understanding the pain. Compassion is about taking away the pain away from others, it’s about solving the problem. That small subtle shift is the difference between a data scientist that can tell you what the graph is doing versus telling you what action you need to do from the insight. That’s a force multiplier by definition. Compassion is also critical for designing beautiful and intuitive products, by solving the pain of the user. Is that how you chose to work in product, as the embodiment of data? I think the first thing that people don’t recognize is that there are a number of people who have started very hard things who also have very deep technical backgrounds. Take Fry’s Electronics for example. John Fry, the founder, is a mathematician. He built a whole castle for one of the mathematical associations out in Morgan Hill, that’s how much of patron of the arts he is for them. Then you can look at Reed Hastings of Netflix, he’s a mathematician. My father and his generation, all of the old Silicon Valley crew were all hardcore scientists. I think it just goes on to show - you look in these odd places and you see things you would not have guessed. I think there’s two roles that have been interesting to me in companies: the first is you’re starting something from scratch and the second is you’re in product. Why those two roles? If you start the company you’re in product by definition, and if you’re in product you’re making. It’s about physically making something. Then the question is, how do you make? There’s a lot of ways and weapons you can use to your advantage. People
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DJ PATIL 11 say there is market assessment, you can do this detailed market assessment, you can identify a gap in the market right there and hit it. There’s marketing products, where you build something and put a lot of whizbang marketing, and the marketing does phenomenally. There are engineering products which are just wow — you can say this is just so well engineered, this is phenomenal, nobody can understand it, but it’s great, pure, raw engineering. There is designing products, creating something beautifully. And then, there’s data. The type of person I like best is the one who has two strong suits in these domains, not just one. Mine, personally, are user experience (UX) and data. Why user experience and data? Most people say you have to be one or the other, and that didn’t make sense to me because the best ways to solve data problems are often with UX. Sometimes, you can be very clever with a UX problem by surfacing data in a very unique way. For example, People You May Know (a viral feature at LinkedIn that connected the social Because of the pace at which the graph between professionals) solved a design world changes, the only way to problem through data. You would join the prepare yourself is by having that site, and it would recommend people to you dynamic range. as you onboard on the website. But People You May Know feels creepy if the results are too good, even it it was just a natural result of an algorithm called triangle closing. They’d ask, “How do you know that? I just met this person!” To fix this, you could say something like “You both know Jake.” Then it’s obvious. It’s a very simplistic design element that fixes the data problem. My belief is that by bringing any two elements together, it’s no longer a world of one. Another way to say this is, how do you create versatility? How do you make people with dynamic range, which is the ability to be useful in many different contexts? The assumption is our careers are naturally changing at a faster rate than we’ve ever seen them change before. Look at the pace at which things are being disrupted. It’s astonishing. When I first got here eBay was the crazy place to be and now they’re on a turnaround. Yahoo went from being the mammoth place to now attempting a turnaround. We’ve had companies that just totally disappeared. I see a spectrum of billion dollar companies coming and going. We’re seeing something very radical happening. Think about Microsoft. Who wouldn’t have killed for a role in Microsoft ten years ago? It was a no brainer. But not anymore. Because of the pace at which the world changes, the only way to prepare yourself is by
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DJ PATIL 12 having that dynamic range. I think what we’re realizing also is that different things give you different elements of dynamic range. Right now data is one of those because it’s so scarce. People are getting the fact that this is happening. It gives a disproportionate advantage to those who are data savvy. You mentioned earlier that when you were looking to become a mathematician you picked a path that optimized for optionality. As a data scientist, what type of skills should one be building to expand or broaden their versatility? I think what data gives you is a unique excuse to interact with many different functions of a business. As a result, you tend to be more in the center and that means you get to understand what lots of different functions are, what other people do, how you can interact with them. In other words, you’re constantly in the fight rather than being relegated to the bench. So you get a lot of time on the field. That’s what changes things. The part here I think people often miss is One of the first things I tell new data that they don’t know how much work this is. scientists when they get into the Take an example from RelateIQ. I’m in the organization is that they better be product role (although they say I’m supposed the first ones in the building and the to be the head of product here, I think of last ones out. these things as team sports and that we’re all in it together), and I work over a hundred hours a week easily. If I had more time I’d go for longer hours. I think one of the things that people don’t recognize is how much net time you just have to put in. It doesn’t matter how old you are or how good you are, you have to put in your time. You’re not putting in your time because of some mythical ten thousand hours thing (I don’t buy that argument at all, I think it’s false because it assumes linear serial learning rather than parallelized learning that accelerates). You put in your time because you can learn a lot more about disparate things that fit into the puzzle together. It’s like a stew, it only becomes good if it’s been simmering for long time. One of the first things I tell new data scientists when they get into the organization is that they better be the first ones in the building and the last ones out. If that means four hours of sleep, get used to it. It’s going to be that way for the first six months, probably a year plus. That’s how you accelerate on the learning curve. Once you get in there, you’re in the conversations. You want to be in those conversations where people are suffering at two in the morning. You’re worn down. They are worn down. All your emotional barriers come down and now you’re really bonding. There’s a reason they put Navy Seals through
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DJ PATIL 13 training hell. They don’t put them in hell during their first firefight. You go into a firefight completely unprepared and you die. You make them bond before the firefight so you can rely on each other and increase their probability of survival in the firefight. It’s not about bonding during the firefight, it’s about bonding before. That’s what I would say about the people you talked to at any of the good data places. They’ve been working 10x harder than most places, because it is do or die. As a result, they have learned through many iterations. That’s what makes them good. What can you do on a day-to-day basis that can make you a good data scientist? I don’t think we know. I don’t think we have enough data on it. I If you watch kids running around a track, and don’t think there’s enough clarity the parents want to leave, the kids always on what works well and what answer, “One more! One more!” You watch doesn’t work well. I think you can an adult run laps, and they are thinking, “How definitely say some things increase many more do I have to do?” the probability of personal success. That’s not just about data science, it’s about listening hard, being a good team player, picking up trash, making sure balls don’t get dropped, taking things off people’s plates, being there for the team rather than as an individual, and focusing on delivering value for somebody or something. When you do that, you have a customer (could be internal, external, anybody). I think that’s what gives you the lift. Besides the usual skills, the other thing that’s really important is the ability to make, storytell, and create narratives. Also, never losing the feeling of passion and curiosity. I think people that go into academia early, go in with passion. You know that moment when you hear a lecture about something, and you’re saying, “Wow! That was mind blowing!” That moment on campus when you’re saying, “Holy crap, I never saw it coming.” Why do we lose that? Here is a similar analogy. If you watch kids running around a track, and the parents want to leave, the kids always answer, “One more! One more!” You watch an adult run laps, and they are thinking, “How many more do I have to do?” You count down the minutes to the workout, instead of saying, “Wow, that was awesome!” I feel that once you flip from one to the other you’ve lost something inherently. You have to really fight hard to fill your day with things that are going to invigorate you on those fronts. One more conversation, one more fight, one more thing. When you find those
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DJ PATIL 14 environments, that’s rare. When you’re around people who are constantly inspiring you with tidbits of information, I feel like that’s when you’re lucky. Is all learning the same? What value can you bring as a young data scientist to people who have more knowledge than yourself? There’s a difference between knowledge and wisdom. I think that’s one of the classic challenges with academia. You can take a high school kid who can build an app better than a person with a doctorate who works in algorithms, and it’s because of their knowledge of the app ecosystem. Wisdom also goes the other way: if you’re working on a very hard academic problem, you can look at it and say, “That’s going to be O(n2)”. I was very fortunate when I was at eBay, as I happened to get inserted in a team where there was a lot of I’m a firm believer in the wisdom. Even though eBay was moving very slowly in apprentice model things we were doing, I was around a lot of people who had a disproportionate amount of wisdom, so I was the stupidest guy with the least amount of tours of duty. But at the same time, I was able to add value because I saw things in ways that they had never seen. So we had to figure out where that wisdom aligned and where it didn’t. The other side of that was at LinkedIn, when you’re on that exponential curve trajectory with a company. People say, “Well you were only at the company for three plus years,” but I happened to be there when it grew from couple hundred to a couple thousand people. Being in a place where you see that crazy trajectory is what gives you wisdom, and that’s the type of thing that I think compounds massively. Many young people today are confronted with this problem related to knowledge and wisdom. They have to decide: Do they do what they’re deeply passionate about in the field they care most about? Or do they do the route that provides them with the most immediate amount of growth? Do they go compound the knowledge of skills, or do they build wisdom in that domain? It’s a good and classic conundrum. I’ve gone with it as a non-linear approach: you go where the world takes you. The way I think about it is, wherever you go, make sure you’re around the best people in the world. I’m a firm believer in the apprentice model, I was very fortunate that I got to train with people like James Yorke who coined with the term “chaos theory.” I was around Sergey Brin’s dad. I was around some really amazing people and their conversations are some of the most critical pieces of input in my life, I think I feel very grateful and fortunate to be
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DJ PATIL 15 around these people. Being around people like Reid Hoffman, Jeff Weiner is what makes you good and that gives you wisdom. So for that tradeoff, if you’re going to be around somebody that’s phenomenal at Google, great! If you’re going to be around someone super phenomenal in the education system, great! Just make sure whatever you are doing, you’re accelerating massively. The derivative of your momentum better be changing fast in the positive direction. It’s all about derivatives. What do you think about risk taking, and defining oneself? Everyone needs to chart their own destiny. The only I thing I think is for certain is that as an individual, you get to ask the questions, and by asking the questions and interpreting the answers, you decide the narrative that is appropriate for you. If the narrative is wrong, it’s your narrative to change. If you don’t like what you’re doing, you get to change it. It may be ugly, maybe hard or painful but the best thing is when you’re younger, you get to take crazy swings at bats that you don’t get to take later on. I couldn’t do half the stuff I was doing before, and I’m very envious of people who get to. And that’s a part of life, there’s the flip side of when you do have family, or responsibilities, that you’re paying If the narrative is wrong, it’s your for that next generation. Your parents put a narrative to change. If you don’t lot on the line to try to stay in a town with like what you’re doing, you get to great schools, and they may not have taken change it. the risk that they would’ve normally taken to do these things. That’s part of the angle by which you play. It’s also the angle which is the difference between what it means as an individual and team player. Sometimes you can’t do the things that you want to do. It’s one of the reasons I’ve become less technical. Take someone like Monica Rogati or Peter Skomoroch, two amazing data scientists and engineers at LinkedIn. What’s a better use of my time? Taking a road block out of their way or me spending time debugging or coding something on my own? In the role I have, in the position and what was expected of me, my job was to remove hurdles from people, my job was to construct the narrative to give other people runway to execute, their job was to execute and they did a hell of a good job at it. You have talked about your research as a way to give back to the public that invested in you. Is there an aspect of the world that you feel like could really use
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