Hugo speakswith Katie Bauer about her time working in data science at both Twitter and Reddit. At the time of recording, Katie was a data science manager at Twitter and prior to that, a founding member of the data team at Reddit. She’s now Head of Data Science at Gloss Genius so congrats on the new job, Katie! In this conversation, we dive into what type of challenges social media companies face that data science is equipped to solve: in doing so, we traverse the difference and similarities in companies such as Twitter and Reddit, the major differences in being an early member of a data team and joining an established data function at a larger organization, the supreme importance of robust measurement and telemetry in data science, along with the mixed incentives for career data scientists, such as building flashy new things instead of maintaining existing infrastructure. I’ve always found conversations with Katie to be a treasure trove of insights into data science and machine learning practice, along with key learnings about data science management. In a word, Katie helps me to understand our space better. In this conversation, she told me that one important function data science can serve in any organization is creating a shared context for lots of different people in the org. We dive deep into what this actually means, how it can play out, traversing the world of dashboards, metric stores, feature stores, machine learning products, the need for top-down support, and much, much more.

Hugo speaks with Mark Saroufim, an Applied AI Engineer at Meta who works on PyTorch where his team’s main focus is making it as easy as possible for people to deploy PyTorch in production outside Meta. Mark first came on our radar with an essay he wrote called Machine Learning: the Great Stagnation, which was concerned with the stagnation in machine learning in academic research and in which he stated Machine learning researchers can now engage in risk-free, high-income, high-prestige work. They are today’s Medieval Catholic priests. This is just the tip of the icebergs of Mark’s critical and often sociological eye and one of the reasons I was excited to speak with him. In this conversation, we talk about the importance of open source software in modern data science and machine learning and how Mark thinks about making it as easy to use as possible. We also talk about risk assessments in considering whether to adopt open source or not, the supreme importance of good documentation, and what we can learn from the world of video game development when thinking about open source. We then dive into the rise of the machine learning cult leader persona, in the context of examples such as Hugging Face and the community they’ve built. We discuss the role of marketing in open source tooling, along with for profit data science and ML tooling, how it can impact you as an end user, and how much of data science can be considered differing forms of live action role playing and simulation. We also talk about developer marketing and content for data professionals and how we see some of the largest names in ML researchers being those that have gigantic Twitter followers, such as Andrei Karpathy. This is part of a broader trend in society about the skills that are required to capture significant mind share these days. If that’s not enough, we jump into how machine learning ideally allows businesses to build sustainable and defensible moats, by which we mean the ability to maintain competitive advantages over competitors to retain market share. In between this interview and its release, PyTorch joined the Linux Foundation, which is something we’ll need to get Mark back to discuss sometime. Links The Myth of Objective Tech Screens Machine Learning: The Great Stagnation Fear the Boom and Bust: Keynes vs. Hayek - The Original Economics Rap Battle! History and the Security of Property by Nick Szabo Mark on YouTube Mark's Substack Mark's Discord

Hugo speaks with Sarah Catanzaro, General Partner at Amplify Partners, about investing in data science and machine learning tooling and where we see progress happening in the space. Sarah invests in the tools that we both wish we had earlier in our careers: tools that enable data scientists and machine learners to collect, store, manage, analyze, and model data more effectively. As you’ll discover, Sarah identifies as a scientist first and an investor second and still believes that her mission is to enable companies to become data-driven and to generate ROI through machine and statistical learning. In her words, she’s still that cuckoo kid who’s ranting and raving about how data and AI will shift every tide. In this conversation, we talk about what scientific inquiry actually is and the elements of playfulness and seriousness it necessarily involves, and how it can be used to generate business value. We talk about Sarah’s unorthodox path from a data scientist working in defense to her time at Palantir and how that led her to build out a data team and function for a venture capital firm and then to becoming a VC in the data tooling space. We then really dive into the data science and machine learning tooling space to figure out why it’s so fragmented: we look to the data analytics stack and software engineering communities to find historical tethers that may be useful. We discuss the moving parts that led to the establishment of a standard, a system of record, and clearly defined roles in analytics and what we can learn from that for machine learning! We also dive into the development of tools, workflows, and division of labour as partial exercises in pattern recognition and how this can be at odds with the variance we see in the machine learning landscape, more generally! Two take-aways are that we need best practices and we need more standardization. We also discussed that, with all our focus and conversations on tools, what conversation we’re missing and Sarah was adamant that we need to be focusing on questions, not solutions, and even questioning what ML is useful for and what it isn’t, diving into a bunch of thoughtful and nuanced examples. I’m also grateful that Sarah let me take her down a slightly dangerous and self-critical path where we riffed on both our roles in potentially contributing to the tragedy of commons we’re all experiencing in the data tooling landscape, me working in tool building, developer relations, and in marketing, and Sarah in venture capital.

Hugo speaks with Hamel Husain, Head of Data Science at Outerbounds, with extensive experience in data science consulting, at DataRobot, Airbnb, and Github. In this conversation, they talk about Hamel's early days in data science, consulting for a wide array of companies, such as Crocs, restaurants, and casinos in Las Vegas, diving into what data science even looked like in 2005 and how you could think about delivering business value using data and analytics back then. They talk about his trajectory in moving to data science and machine learning in Silicon Valley, what his expectations were, and what he actually found there. They then take a dive into AutoML, discussing what should be automated in Machine learning and what shouldn’t. They talk about software engineering best practices and what aspects it would be useful for data scientists to know about. They also got to talk about the importance of literate programming, notebooks, and documentation in data science and ML. All this and more! Links Hamel on twitter The Outerbounds documentation project repo Practical Advice for R in Production nbdev: Create delightful python projects using Jupyter Notebooks

Hugo speaks with Peter Wang, CEO of Anaconda, about what the value proposition of data science actually is, data not as the new oil, but rather data as toxic, nuclear sludge, the fact that data isn’t real (and what we really have are frozen models), and the future promise of data science. They also dive into an experimental conversation around open source software development as a model for the development of human civilization, in the context of developing systems that prize local generativity over global extractive principles. If that’s a mouthful, which it was, or an earful, which it may have been, all will be revealed in the conversation. LInks Peter on twitter Anaconda Nucleus Jordan Hall on the Jim Rutt Show: Game B Meditations On Moloch -- On multipolar traps Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky Finite and Infinite Games by James Carse Governing the Commons: The Evolution of Institutions for Collective Action by Elinor Olstrom Elinor Ostrom's 8 Principles for Managing A Commmons Haunted by Data, a beautiful and mesmerising talk by Pinboard.in founder Maciej Ceglowski

Hugo speaks with Peter Wang, CEO of Anaconda, about how Python became so big in data science, machine learning, and AI. They jump into many of the technical and sociological beginnings of Python being used for data science, a history of PyData, the conda distribution, and NUMFOCUS. They also talk about the emergence of online collaborative environments, particularly with respect to open source, and attempt to figure out the movings parts of PyData and why it has had the impact it has, including the fact that many core developers were not computer scientists or software engineers, but rather scientists and researchers building tools that they needed on an as-needed basis They also discuss the challenges in getting adoption for Python and the things that the PyData stack solves, those that it doesn’t and what progress is being made there. People who have listened to Hugo podcast for some time may have recognized that he's interested in the sociology of the data science space and he really considered speaking with Peter a fascinating opportunity to delve into how the Pythonic data science space evolved, particularly with respect to tooling, not only because Peter had a front row seat for much of it, but that he was one of several key actors at various different points. On top of this, Hugo wanted to allow Peter’s inner sociologist room to breathe and evolve in this conversation. What happens then is slightly experimental – Peter is a deep, broad, and occasionally hallucinatory thinker and Hugo wanted to explore new spaces with him so we hope you enjoy the experiments they play as they begin to discuss open-source software in the broader context of finite and infinite games and how OSS is a paradigm of humanity’s ability to create generative, nourishing and anti-rivlarous systems where, by anti-rivalrous, we mean things that become more valuable for everyone the more people use them! But we need to be mindful of finite-game dynamics (for example, those driven by corporate incentives) co-opting and parasitizing the generative systems that we build. These are all considerations they delve far deeper into in Part 2 of this interview, which will be the next episode of VG, where we also dive into the relationship between OSS, tools, and venture capital, amonh many others things. LInks Peter on twitter Anaconda Nucleus Calling out SciPy on diversity (even though it hurts) by Juan Nunez-Iglesias Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky Finite and Infinite Games by James Carse Governing the Commons: The Evolution of Institutions for Collective Action by Elinor Olstrom Elinor Ostrom's 8 Principles for Managing A Commmons

Hugo speaks with Jacqueline Nolis, Chief Product Officer at Saturn Cloud (formerly Head of Data Science), about all types of failure modes in data science, ML, and AI, and they delve into bullshit jobs in data science (yes, that’s a technical term, as you’ll find out) –they discuss the elements that are bullshit, the elements that aren’t, and how to increase the ratio of the latter to the former. They also talk about her journey in moving from mainly working in prescriptive analytics building reports in PDFs and power points to deploying machine learning products in production. They delve into her motion from doing data science to designing products for data scientists and how to think about choosing career paths. Jacqueline has been an individual contributor, a team lead, and a principal data scientist so has a lot of valuable experience here. They talk about her experience of transitioning gender while working in data science and they work hard to find a bright vision for the future of this industry! Links Jacqueline on twitter Building a Career in Data Science by Jacqueline and Emily Robinson Saturn Cloud Why are we so surprised?, a post by Allen Downey on communicating and thinking through uncertainty Data Mishaps Night! The Trump administration’s “cubic model” of coronavirus deaths, explained by Matthew Yglesias Working Class Deep Learner by Mark Saroufim

Hugo speaks with Jim Savage, the Director of Data Science at Schmidt Futures, about the need for data science in executive training and decision, what data scientists can learn from economists, the perils of "data for good", and why you should always be integrating your loss function over your posterior. Jim and Hugo talk about what data science is and isn’t capable of, what can actually deliver value, and what people really enjoy doing: the intersection in this Venn diagram is where we need to focus energy and it may not be quite what you think it is! They then dive into Jim's thoughts on what he dubs Executive Data Science. You may be aware of the slicing of the data science and machine learning spaces into descriptive analytics, predictive analytics, and prescriptive analytics but, being the thought surgeon that he is, Jim proposes a different slicing into (1) tool building OR data science as a product, (2) tools to automate and augment parts of us, and (3) what Jim calls Executive Data Science. Jim and Hugo also talk about decision theory, the woeful state of causal inference techniques in contemporary data science, and what techniques it would behoove us all to import from econometrics and economics, more generally. If that’s not enough, they talk about the importance of thinking through the data generating process and things that can go wrong if you don’t. In terms of allowing your data work to inform your decision making, thery also discuss Jim’s maxim “ALWAYS BE INTEGRATING YOUR LOSS FUNCTION OVER YOUR POSTERIOR” Last but definitively not least, as Jim has worked in the data for good space for much of his career, they talk about what this actually means, with particular reference to fast.ai founder & QUT professor of practice Rachel Thomas’ blog post called “Doing Data Science for Social Good, Responsibly”. Rachel’s post takes as its starting point the following words of Sarah Hooker, a researcher at Google Brain: "Data for good" is an imprecise term that says little about who we serve, the tools used, or the goals. Being more precise can help us be more accountable & have a greater positive impact. And Jim and I discuss his work in the light of these foundational considerations. Links Jim on twitter What Is Causal Inference?An Introduction for Data Scientists by Hugo Bowne-Anderson and Mike Loukides Jim's must-watch Data Council talk on Productizing Structural Models [Mastering Metrics}(https://www.masteringmetrics.com/) by Angrist and Pischke Mostly Harmless Econometrics: An Empiricist's Companion by Angrist and Pischke The Book of Why by Judea Pearl Decision-Making in a Time of Crisis by Hugo Bowne-Anderson Doing Data Science for Social Good, Responsibly by Rachel Thomas

Hugo speaks with Heather Nolis, Principal Machine Learning engineer at T-mobile, about what data science, machine learning, and AI look like at T-mobile, along with Heather’s path from a software development intern there to principal ML engineer running a team of 15. They talk about: how to build a DS culture from scratch and what executive-level support looks like, as well as how to demonstrate machine learning value early on from a shark tank style pitch night to the initial investment through to the POC and building out the function; all the great work they do with R and the Tidyverse in production; what it’s like to be a lesbian in tech, and about what it was like to discover she was autistic and how that impacted her work; how to measure and demonstrate success and ROI for the org; some massive data science fails!; how to deal with execs wanting you to use the latest GPT-X – in a fragmented tooling landscape; how to use the simplest technology to deliver the most value. Finally, the team just hired their first FT ethicist and they speak about how ethics can be embedded in a team and across an institution. Links Put R in prod: Tools and guides to put R models into production Enterprise Web Services with Neural Networks Using R and TensorFlow Heather on twitter T-Mobile is hiring! Hugo's upcoming fireside chat and AMA with Hilary Parker about how to actually produce sustainable business value using machine learning and product management for ML!

Rachael Tatman is a senior developer advocate for Rasa, where she’s helping developers build and deploy ML chatbots using their open source framework. Rachael has a PhD in Linguistics from the University of Washington where her research was on computational sociolinguistics, or how our social identity affects the way we use language in computational contexts. Previously she was a data scientist at Kaggle and she’s still a Kaggle Grandmaster. In this conversation, Rachael and I talk about the history of NLP and conversational AI//chatbots and we dive into the fascinating tension between rule-based techniques and ML and deep learning – we also talk about how to incorporate machine and human intelligence together by thinking through questions such as “should a response to a human ever be automated?” Spoiler alert: the answer is a resounding NO WAY! In this journey, something that becomes apparent is that many of the trends, concepts, questions, and answers, although framed for NLP and chatbots, are applicable to much of data science, more generally. We also discuss the data scientist’s responsibility to end-users and stakeholders using, among other things, the lens of considering those whose data you’re working with to be data donors. We then consider what globalized language technology looks like and can look like, what we can learn from the history of science here, particularly given that so much training data and models are in English when it accounts for so little of language spoken globally. Links Rachael's website Rasa Speech and Language Processing by Dan Jurafsky and James H. Martin Masakhane, putting African languages on the #NLP map since 2019 The Distributed AI Research Institute, a space for independent, community-rooted AI research, free from Big Tech’s pervasive influence The Algorithmic Justice League, unmasking AI harms and biases Black in AI, increasing the presence and inclusion of Black people in the field of AI by creating space for sharing ideas, fostering collaborations, mentorship and advocacy Hugo's blog post on his new job and why it's exciting for him to double down on helping scientists do better science