Data Journeys

Drew Conway is a world-renowned data scientist, entrepreneur, author, and speaker, perhaps most well-known for his infamous 2010 "Data Science Venn Diagram".

Today, Drew is the Founder & CEO of Alluvium: a company using machine learning and AI to turn massive streams of data produced by industrial operations companies into insights that bridge the gap between big data and human expertise.

Designed with the goal of helping industrial operations become safer, more efficient and more profitable, the Alluvium platform makes industrial machine data meaningful and useful to the people who rely on it to make decisions that affect the stability of their operation.

Before starting Alluvium in 2015, Drew helped start:

• Data Gotham: an organization focused on supporting the NYC data community, with an annual conference bringing together people from all industries
• DataKind: a non-profit that brings high-impact organizations together with leading data scientists to use data science in the service of humanity. They enable data scientists and social changemakers to address tough humanitarian challenges together, ranging from education to poverty, health to human rights, and the environment to cities.

After starting the conversation by exploring Drew's early years, we focused most of the dialogue around his (quite frankly, brilliant) thought process around identifying the highest-impact, most-needed applications for data science across problem spaces.

Some of my favorite talking points included:

• Why "force of will" and a "tendency toward combativeness" were key to Drew's early development and overcoming imposter syndrome
• Lessons learned from his 4th grade teacher who told him he was bad at math and an AAU basketball coach who made his team find their way home from the outskirts of Las Vegas
• The questions Drew asks executives who tell him they want to hire a data science team, how he recommends they avoid being "seduced by the industry" and "return back to first principles"
• Drew's process for determining new applications for data science within various industries
• The three-question mental model Drew used to identify Alluvium's first major product offering: business problem → data available → human support
• Alluvium's team-building and hiring philosophy, how it's evolved from day one until today
• The story behind DataKind, how he and his team decided what nonprofits to start by working with, and the step-by-step process they took to testing their assumptions

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep14/

Drew's LinkedIn: https://www.linkedin.com/in/drew-conway-13b5b013/

AJ's Twitter: https://twitter.com/ajgoldstein393/

Anthony Scriffignano is the Chief Data Scientist at Dun & Bradstreet: a publicly traded company that provides commercial data, analytics and insights about businesses through their database of more than 290 million business records worldwide. Most commonly known as D&B, the company was originally founded 176 years ago in the horse-and-buggy days of 1841, and today, is headquartered in Short Hills, NJ, with over $2.5 billion in assets.

Anthony has been at D&B since 2002 and, with over 35 years of experience in information technologies, Big-4 management consulting, and international business, is well-regarded as an international thought-leader in data science.

Today he leads a team of data scientists focused on advancing Dun & Bradstreet's core capabilities and IP globally. With extensive background in advanced algorithms and linguistics, he holds multiple patents and presents globally on data and technology trends, multilingual challenges in business identity, and artificial intelligence.

In this conversation, we spoke quite a bit about the international lens through which Anthony views his work, covering topics like:

• How foreign language, cultural awareness, and learning to deeply understand people with very different backgrounds than his own has affected his work as a data scientist
• Anthony's own definition of 'trust', how he's seen it vary across cultures, and how this played out in "acceptance testing" at Dun & Bradstreet
• Why "technical expertise is necessary but not sufficient" and how his global perspective (he knows 7 languages & does quite a bit of travel!) has supplemented day-to-day work
• The bedrock foundations on which he sees the becoming of a data scientist today: People // Process // Technology // Mindset
• How he's managed to stay focused on the 'critical few', rather than the 'trivial many' throughout his career, especially in an increasingly distracted world
• Why studying martial arts is the best investment he's ever made in himself

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep13/

Anthony's LinkedIn: https://www.linkedin.com/in/anthony-scriffignano-ph-d-9165845/

AJ's Twitter: https://twitter.com/ajgoldstein393/

Favio Vazquez is the Principal Data Scientist at OXXO: Mexico's largest convenience store chain with over 17,500 locations.

In addition to his work at OXXO, Favio brings new meaning to the term "polymath" by simultaneously holding 5 other related positions at AI companies in Mexico:

• Senior Data Scientist @ Raken Data Group
• Chief Data Scientist @ Iron AI
• Creator of the Ciencias y Datos online course
• Data Science Lecturer @ Afi Escuela de Finanzas
• Collaborator @ CosmoSIS

With such a wide-ranging background, we covered a lot in this conversation, including:

• Favio's experience growing up in Venezuela, the childhood influencers that played a core part in shaping him into who he is today
• Tricks like "frontloading" and "batching" that Favio uses to juggle many projects at once
• How individuals looking to get hired as a company's first data scientist should explain the value that data science can provide, what language to use and terminology to avoid
• His approach for determining which questions would yield the most valuable return in applying data science to OXXO's business
• The greatest similarities and differences between how data science is practiced in Mexico versus the United States
• When and how beginners should invest in learning advanced topics like big data processing (e.g. Apache Spark) and deep learning (e.g. neural networks)

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep11/

Favio's LinkedIn: https://www.linkedin.com/in/faviovazquez/

AJ's Twitter: https://twitter.com/ajgoldstein393/

Dan Wulin is the Director of Data Science at Wayfair: an international e-commerce company specializing in home goods.

Wayfair is a $5B company growing 40% year-over-year, with 10 million products and over 8,700 employees around the world. Their data science team is 80-people strong and growing fast, using econometrics to optimize prices, biostatistics to boost marketing, and computer vision to personalize product recommendations.

Prior to joining Wayfair, Dan studied Math & Physics as an undergraduate at Columbia University and, thereafter, received his PhD in Theoretical Physics from the University of Chicago. Coming out of school, he worked as a consultant at Boston Consulting Group for a year in Chicago before transitioning to Wayfair in Boston.

In this conversation, we cover a wide-range of topics, including:

• His childhood obsession with text-based multiplayer RPGs like Gemstone
• Dan's roots in academia, how studying the physics of superconductors taught him (painfully) how to break down complex problems in simple ways
• How a misalignment of his problem solving approach with standard consulting frameworks at BCG led him to Wayfair
• Dan's first major effort at Wayfair, project Athena, that saved the company millions of dollars on Google ad spending
• His passion for setting up Wayfair's data science team to be successful, how teams within the organization partner, as well as who and how they hire
• The computer-vision-driven approach Wayfair takes toward measuring people's creative taste on subjective things like furniture preference

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep11/

Dan's LinkedIn: https://www.linkedin.com/in/dan-wulin-02169a2a/

AJ's Twitter: https://twitter.com/ajgoldstein393/

Jure Leskovec is the Chief Scientist of Pinterest, an $11 billion dollar company hosting over 75 billion idea "pins" from it's 175 million monthly users worldwide. Jure originally arrived at Pinterest in 2014 when his company, Kosei, was acquired after starting a "recommendation revolution" through smarter, personalized mobile ads.

When Jure is not "turning cameras into keyboards" at Pinterest -- Fast Company's "2nd most innovative AI company" -- he can also be found fulfilling his responsibilities as a:

• Associate Professor of Computer Science at Stanford University - where his research focuses on mining and modeling large social and information networks, including relationship graphs and chain effects in online community settings
• Investigator at the Chan-Zuckerberg Biohub - a multidisciplinary research organization on a mission to make all diseases preventable, manageable or curable by the year 2100

Some favorite topics we covered include:

• How being Pinterest's Chief Data Scientist has affected his own social media use
• The story behind how he went from party conversation about Kosei to company acquisition by Pinterest in just 2 months
• How Pinterest makes recommendations to it's users and thinks about the explore vs. exploit tradeoff of social media ads
• Moral considerations of echo chambers/filter bubbles/confirmation bias that he and his team take into account when serving Pinterest's 200+ million users content each day
• Balancing short and long-term benefits at the individual (value to user), community (health of the content ecosystem), & company (ad revenue) level
• Recent research from Harvard, Stanford, UChicago, & Cornell investigating how machine learning can help criminal court judges make high-stake decisions

This turned out to be one of my most fascinating conversations yet, so I hope you enjoy it as much as I did!

Show Notes: https://ajgoldstein.com/podcast/ep10/

Jure's Twitter: https://twitter.com/jure?lang=en

AJ's Twitter: https://twitter.com/ajgoldstein393/

Daniela Huppenkothen is the Associate Director at the DIRAC Institute of University of Washington, home to a diverse team of researchers in astrophysics and cosmology.

Before arriving at DIRAC, she has studied space & data science as a:

• Moore-Sloan Data Science Fellow of NYU - a multi-dimensional and independent research programs with impact in several scientific domains
• PhD recipient at University of Amsterdam - where she researched high & low energy astrophysics and the relationship between the two communities, namely "magnetar bursts".
• Blogger of Hackathons, Data Science, and Academia - Daniela takes on a number of issues and shares her wise life lessons along the way.

In this conversation, we cover a wide-range of topics, including:

• The all-too-common (but remarkably important) mix up between astrology and astronomy
• How data science has a place in astronomy, with telescopes more powerful than several hundred iphones
• Frustrations with academia, a lack of validity when it comes to standardized testing, and how one learns to love getting harsh criticism
• How astronomy is an exciting opportunity to build teams who collaborate in unexpected ways, and tie together multiple backgrounds
• How Daniela's academic outlook developed from open source - without necessarily realizing it in the moment

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep9/

Daniela's Website: http://huppenkothen.org/

Daniela's LinkedIn: https://www.linkedin.com/in/daniela-huppenkothen-0b44a97b/

AJ's Twitter: https://twitter.com/ajgoldstein393/

Chris Albon is the Chief Data Scientist of BRCK: a Kenyan startup building a network dedicated to connecting Africa to the internet, and author of the Machine Learning with Python Cookbook.

For years he's been contributing to the data science world through so many different outlets:

• Cofounder of New Knowledge AI - an social media platform focused on protecting companies from disinformation, fighting fake news, and defending public discourse

• Former host of Partially Derivative - a popular podcast mixing explorations into data science techniques with discussions in the field's leading experts.

• Content creator of Machine Learning Flashcards - simplified, easy to digest flashcards for otherwise-complex machine learning concepts.

• Blog writer at chrisalbon.com - providing some of best (and definitely most wide-ranging) technical notes out there on machine learning, statistics, deep learning, Python, and so much more.

Our conversation went many places, including:

• How early childhood experiences (including his heritage in Zimbabwe) led him to focus his career so strictly on social impact, through political and humanitarian efforts
• How he's established himself (and his blog) as an authority figure in data science & AI (with 523 resource links and counting) without having a "technical" degree
• The step-by-step process BRCK takes when incorporating technology into African communities & what he's learned about challenging his own assumptions while doing so
• The initial inspiration behind starting the Partially Derivative podcast and why his aim was for it to be the "talk at the bar after the conference"
• what his newest book -- Machine Learning Cookbook with Python -- is all about, who it's for, and the gap it addresses for the community

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep8/

Chris' Twitter: https://twitter.com/chrisalbon

AJ's Twitter: https://twitter.com/ajgoldstein393/

Fernando Perez is best-known as the creator of IPython and co-founder of Project Jupyter: a set of open-source data science tools that some may consider to be the equivalent of the bat & ball to the sport of baseball. Today, you really can't play the game of data science without Jupyter Notebooks and our guest today is one of Jupyter's leads and originators (see here for the rest of the amazing team).

Fernando is also an Assistant Professor in Statistics at UC Berekely, Researcher at the Berekely Institute for Data Science, and Founding Board Member of the NumFOCUS foundation — the community that creates the SciPy stack, along with virtually every other notable open source data science tool out there.

This conversation was recorded in-person with Fernando in his office on UC Berekely's campus, and it turned out to be the most humanizing, energizing, and down-to-earth interview I've had so far.

Some of the many topics we covered include:

• what Fernando wanted to be while growing up in Medellin (Me-de-jean), Colombia
• the function that formal education played in his learning of data science
• the story behind IPython and Project Jupyter and it's evolution over the past 10 years
• lessons learned about technical competence and human character from his mentors over the years
• what a "computational narrative" means to him and why it's principles are key to data storytelling
• Fernando's experience teaching a 650-student course (part of a pair of courses that are the largest of it's kind) as part of the Berekely Institute of Data Science

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep7/

Fernando's Twitter: https://twitter.com/fperez_org

AJ's Twitter: www.twitter.com/ajgoldstein393/

Andrew Ng is an Adjunct Professor at Stanford University and nothing short of a giant in the data science, machine learning, and artificial intelligence world.

For the past decade he's been shaping the way we live and learn. Four recent examples include:

• Co-Founder of Coursera: an education platform that offers online courses from top universities across the world
• Chairman of the Board for Woebot: a chatbot that's currently revolutionizing mental health care
• Creator of DeepLearning.AI: a series of specialization courses created to help beginners break into the field of AI
• Former Chief Scientist at Baidu AI Group: basically the Google of China

This conversation was recorded in-person with Andrew in his office on Stanford's campus in Palo Alto, California.

We covered a ton of different topics, including:

• the goals of Andrew's new $175M AI Fund
• how he plans to revolutionize manufacturing
• strategies for ML practitioners to tighten their feedback loop
• what differentiates the best businesses like Amazon, Facebook, & Google from the rest in terms of their use of AI
• the lack of progress he's seeing toward artificial general intelligence and the so called "technology singularity"
• why going to college for 4 years and coasting for 40 years makes no sense in today's rapidly changing world

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep6/

Andrew's LinkedIn: https://twitter.com/AndrewYNg

AJ's Twitter: www.twitter.com/ajgoldstein393/

Eric Weber is a Senior Data Scientist at LinkedIn, online educator, and epitome of a lifelong learner who's created quite the following for himself through posts on LinkedIn's platform.

He holds a Bachelors degree in Mathematics at the University of Wisconsin, two Masters degrees in Math and Business Analytics at Arizona State University and the University of Minnesota, and a PhD from ASU. And as if that's all not enough, he's obtained 31 course certificates in MOOCs from Coursera and DataCamp… nearly all which in the realm of Data Science.

Naturally, this conversation was primarily focused on Data Science education through the lens of Eric's experience as both a teacher & student.

Some topics we covered include…

• Eric'c childhood obsessions, what he wanted to be growing up
• what attracted Eric to LinkedIn and how he landed the job
• the skills & aptitudes best suited for in-class vs. online learning
• strategies for approaching online & self-directed learning
• the best and worst advice Eric has heard or received
• ethical implication of product promotion
• the danger of "content farms" in maintaining authentic perspectives

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep5/

Eric's LinkedIn: https://www.linkedin.com/in/eric-weber-060397b7/

AJ's Twitter: www.twitter.com/ajgoldstein393/