Towards Data Science

Most of us want to change our identities. And we usually have an idealized version of ourselves that we aspire to become — one who’s fitter, smarter, healthier, more famous, wealthier, more centered, or whatever.

But you can’t change your identity in a fundamental way without also changing what you do in your day-to-day life. You don’t get fitter without working out regularly. You don’t get smarter without studying regularly.

To change yourself, you must first change your habits. But how do you do that?

Recently, books like Atomic Habits and Deep Work have focused on answering that question in general terms, and they’re definitely worth reading. But habit formation in the context of data science, analytics, machine learning, and startups comes with a unique set of challenges, and deserves attention in its own right. And that’s why I wanted to sit down with today’s guest, Russell Pollari.

Russell may now be the CTO of the world’s largest marketplace for income share mentorships (and the very same company I work at every day!) but he was once — and not too long ago — a physics PhD student with next to no coding ability and a classic case of the grad school blues. To get to where he is today, he’s had to learn a lot, and in his quest to optimize that process, he’s focused a lot of his attention on habit formation and self-improvement in the context of tech, data science and startups.

Revenues drop unexpectedly, and management pulls aside the data science team into a room. The team is given its marching orders: “your job,” they’re told, “is to find out what the hell is going on with our purchase orders.”

That’s a very open-ended question, of course, because revenues and signups could drop for any number of reasons. Prices may have increased. A new user interface might be confusing potential customers. Seasonality effects might have to be considered. The source of the problem could be, well, anything.

That’s often the position data scientists find themselves in: rather than having a clear A/B test to analyze, they frequently are in the business of combing through user funnels to ensure that each stage is working as expected.

It takes a very detail-oriented and business-savvy team to pull off an investigation with that broad a scope, but that’s exactly what Medium has: a group of product-minded data scientists dedicated to investigating anomalies and identifying growth opportunities hidden in heaps of user data. They were kind enough to chat with me and talk about how Medium does data science for this episode of the Towards Data Science podcast.

If you want to know where data science is heading, it helps to know where it’s been. Very few people have that kind of historical perspective, and even fewer combine it with an understanding of cutting-edge tooling that hints at the direction the field might be taking in the future.

Luckily for us, one of them is Cameron Davidson-Pillon, the former Director of Data Science at Shopify. Cameron has been knee-deep in data science and estimation theory since 2012, when the space was still coming into its own. He’s got a great high-level perspective not only on technical issues but also on hiring and team-building, and he was kind enough to join us for today’s episode of the Towards Data Science podcast.

It’s easy to think of data science as a purely technical discipline: after all, it exists at the intersection of a number of genuinely technical topics, from statistics to programming to machine learning.

But there’s much more to data science and analytics than solving technical problems — and there’s much more to the data science job search than coding challenges and Kaggle competitions as well. Landing a job or a promotion as a data scientist calls on a ton of career skills and soft skills that many people don’t spend nearly enough time honing.

On this episode of the podcast, I spoke with Emily Robinson, an experienced data scientist and blogger with a pedigree that includes Etsy and DataCamp, about career-building strategies. Emily’s got a lot to say about the topic, particularly since she just finished authoring a book entitled “Build a Career in Data Science” with her co-author Jacqueline Nolis. The book explores a lot of great, practical strategies for moving data science careers forward, many of which we discussed during our conversation.

Most of us believe that decisions that affect us should be made rationally: they should be reached by following a reasoning process that combines data we trust with a logic that we find acceptable.

As long as human beings are making these decisions, we can probe at that reasoning to find out whether we agree with it. We can ask why we were denied that bank loan, or why a judge handed down a particular sentence, for example.

Today however, machine learning is automating away more and more of these important decisions, and as a result, our lives are increasingly governed by decision-making processes that we can’t interrogate or understand. Worse, machine learning algorithms can exhibit bias or make serious mistakes, so a black-box-ocracy risks becoming more like a dystopia than even the most imperfect human-designed systems we have today.

That’s why AI ethics and AI safety have drawn so much attention in recent years, and why I was so excited to talk to Alayna Kennedy, a data scientist at IBM whose work is focused on the ethics of machine learning, and the risks associated with ML-based decision-making. Alayna has consulted with key players in the US government’s AI effort, and has expertise applying machine learning in industry as well, through previous work on neural network modelling and fraud detection.

In mid-January, China launched an official investigation into a string of unusual pneumonia cases in Hubei province. Within two months, that cluster of cases would snowball into a full-blown pandemic, with hundreds of thousands — perhaps even millions — of infections worldwide, with the potential to unleash a wave of economic damage not seen since the 1918 Spanish influenza or the Great Depression.

The exponential growth that led us from a few isolated infections to where we are today is profoundly counterintuitive. And it poses many challenges for the epidemiologists who need to pin down the transmission characteristics of the coronavirus, and for the policy makers who must act on their recommendations, and convince a generally complacent public to implement life-saving social distancing measures.

With the coronas in full bloom, I thought now would be a great time to reach out to Jeremy Howard, co-founder of the incredibly popular Fast.ai machine learning education site. Along with his co-founder Rachel Thomas, Jeremy authored a now-viral report outlining a data-driven case for concern regarding the coronavirus.

It’s easy to think of data scientists as “people who explore and model data”. Bur in reality, the job description is much more flexible: your job as a data scientist is to solve problems that people actually have with data.

You’ll notice that I wrote “problems that people actually have” rather than “build models”. It’s relatively rare that the problems people have actually need to be solved using a predictive model. Instead, a good visualization or interactive chart is almost always the first step of the problem-solving process, and can often be the last as well.

And you know who understands visualization strategy really, really well? Plotly, that’s who. Plotly is a company that builds a ton of great open-source visualization, exploration and data infrastructure tools (and some proprietary commercial ones, too). Today, their tooling is being used by over 50 million people worldwide, and they’ve developed a number of tools and libraries that are now industry standard. So you can imagine how excited I was to speak with Plotly co-founder and Chief Product Officer Chris Parmer.

Chris had some great insights to share about data science and analytics tooling, including the future direction he sees the space moving in. But as his job title suggests, he’s also focused on another key characteristic that all great data scientists develop early on: product instinct (AKA: “knowing what to build next”).

Most machine learning models are used in roughly the same way: they take a complex, high-dimensional input (like a data table, an image, or a body of text) and return something very simple (a classification or regression output, or a set of cluster centroids). That makes machine learning ideal for automating repetitive tasks that might historically have been carried out only by humans.

But this strategy may not be the most exciting application of machine learning in the future: increasingly, researchers and even industry players are experimenting with generative models, that produce much more complex outputs like images and text from scratch. These models are effectively carrying out a creative process — and mastering that process hugely widens the scope of what can be accomplished by machines.

My guest today is Xander Steenbrugge, and his focus is on the creative side of machine learning. In addition to consulting with large companies to help them put state-of-the-art machine learning models into production, he’s focused a lot of his work on more philosophical and interdisciplinary questions — including the interaction between art and machine learning. For that reason, our conversation went in an unusually philosophical direction, covering everything from the structure of language, to what makes natural language comprehension more challenging than computer vision, to the emergence of artificial general intelligence, and how all these things connect to the current state of the art in machine learning.

I can’t remember how many times I’ve forgotten something important.

I’m sure it’s a regular occurrence though: I constantly forget valuable life lessons, technical concepts and useful bits of statistical theory. What’s worse, I often forget these things after working bloody hard to learn them, so my forgetfulness is just a giant waste of time and energy.

That’s why I jumped at the chance to chat with Iain Harlow, VP of Science at Cerego — a company that helps businesses build training courses for their employees by optimizing the way information is served to maximize retention and learning outcomes.

Iain knows a lot about learning and has some great insights to share about how you can optimize your own learning, but he’s also got a lot of expertise solving data science problems and hiring data scientists — two things that he focuses on in his work at Cerego. He’s also a veteran of the academic world, and has some interesting observations to share about the difference between research in academia and research in industry.

You train your model. You check its performance with a validation set. You tweak its hyperparameters, engineer some features and repeat. Finally, you try it out on a test set, and it works great!

Problem solved? Well, probably not.

Five years ago, your job as a data scientist might have ended here, but increasingly, the data science life cycle is expanding to include the steps after basic testing. This shouldn’t come as a surprise: now that machine learning models are being used for life-or-death and mission-critical applications, there’s growing pressure on data scientists and machine learning engineers to ensure that effects like feature drift are addressed reliably, that data science experiments are replicable, and that data infrastructure is reliable.

This episode’s guest is Luke Marsden, and he’s made these problems the focus of this work. Luke is the founder and CEO of Dotscience, a data infrastructure startup that’s creating a git-like tool for data science version control. Luke has spent most of his professional life working on infrastructure problems at scale, and has a lot to say about the direction data science and MLOps are heading in.