Klaviyo Data Science Podcast

Games typically involve some combination of math, chance, strategy, and skill. In this two part mini-series, the Klaviyo Data Science Podcast investigates the role of math in games and how our relationship to them is changing given the increasing number of sophisticated AI Agents that can often beat us at our favorite games. This podcast additionally covers concepts ranging from what makes a game...

All companies start small. The lucky ones grow, and growth necessarily comes with change. This month on the Klaviyo Data Science Podcast, we look at Klaviyo’s growth through the past 8 years and the profound effects that growth has had on R&D, ranging from the tooling we use to the processes we employ to the very culture we strive to cultivate. Listen in to learn: How the day-to-day of some of...

This month, we return to a classic Klaviyo Data Science Podcast series: books every data scientist (and software engineer) should read. This episode focuses on the Clean * duology by Robert C. Martin, which teaches the principles of both clean code and clean architecture. We’ve brought on two senior engineers at Klaviyo who’ve learned, practiced, and developed their own opinions on the lessons in...

What should I do next? A common question, one that seems simple on the surface, but the answer, especially a more optimal answer, can be very difficult to uncover. It may involve information that the asker is not aware of, be unintuitive, or even be counter to our instincts. This month, we discuss a new Klaviyo feature: Next Best Action. Utilizing Klaviyo’s knowledge of marketing best practices, w...

All successful teams have at least one leader, and most have at least one manager. This episode, we dive into how leadership works on highly technical teams, how managing a highly technical team works, and why the two aren’t exactly the same thing. Listen along for more discussion about: The traits of highly effective leaders — and how that might look different on an engineering or data science te...

What is agile methodology — and, just as importantly, what is it not? Whether you’re new to agile entirely or you stay up late pondering its most philosophical inner workings, if you want to know more about agile and how organizations can reap its benefits while avoiding its pitfalls, this is the episode for you. You’ll learn about a variety of topics, including: How to effectively compromise betw...

This month, the Klaviyo Data Science Podcast welcomes Evan Miller to deliver a seminar on his recently published paper, Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations ! This episode is a mix of a live seminar Evan gave to the team at Klaviyo and an interview we conducted with him afterward. Suppose you’re trying to understand the performance of an AI model — maybe...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… 2024 Year in Review As the new year starts, we take a look back at 2024. We spoke to data scientists and people who work closely with data scientists, and we asked them all the question we ask every year: what is the coolest data science thing you learned about in 2024? You’ll hear a wide range of answers, including: How...

How we protect invention and ingenuity: Patents Writing software often involves taking known patterns, combining and shaping them, and adding needed context or specialization related to the problem we’re trying to solve. Sometimes, that means writing something that’s effectively been written by someone else before. But sometimes, that means creating something new. What should you do in a case wher...

Welcome to the November episode of the Klaviyo Data Science Podcast for 2024! In years past, November episodes reflected the chaotic Black Friday/Cyber Monday season by examining unique challenges of readiness, scale, and fundamental changes happening with little to no warning, as well as how those challenges were handled; this November is no different. What happens when two of the largest email p...

As most data scientists will tell you, there is no such thing as the single best model or the perfect model . Some work well in some circumstances but poorly in others, some present a specific tradeoff between factors like flexibility and explainability that is only useful in certain settings. Some are best set up to handle specific types of data that don’t arise in every single project.  But at t...

If you’re making software, especially data science-powered software, there’s a good chance one of your biggest goals is to empower stronger and deeper personalization for your users. Our topic for this month: how can you do even more than that? How can we make personalization not just robust, but both more effective and easier than the alternative?  It’s not a simple task, but it is one that the t...

It may come as a suprise to those of you reading this, but this milestone snuck up on me. I was surprised to realize we’d reached a full 50 episodes. What better time to take a moment to reflect and look back? This episode is all about the Klaviyo Data Science Podcast. We talk through the history of the podcast, how we approach making episodes that matter to our listeners, our highlight episodes,...

A big part of growing and developing as a data scientist, or any other member of a data science team, is taking time to reflect, learn, and distill experiences into advice. This month, we’ve asked four senior members of the data science team to do exactly that: look back over their careers, reflect on what they know and what they wished they’d known earlier, and tell everyone what those lessons ar...

Internationalizing your product There are many aspects of product growth — reaching new heights for peak volume, reaching new levels of sustained daily volume, growing your feature set and the complexity of your code based, and many others. Dealing with growth in an intelligent and forward-looking way is never easy, but this month we deal with a type of growth that presents its own unique set of c...

How real marketers use data science We spend a lot of time on this podcast talking about how to build data science solutions. Implicit in many of those conversations is perhaps the most fundamental truth of product design and development: we build data science solutions because people use them. We aren’t doing this just for fun — the reason we spend so much time, effort, and energy to refine our s...

An Introduction to ML Ops  Building data science products requires many things we’ve discussed on this podcast before: insight, customer empathy, strategic thinking, flexibility, and a whole lot of determination. But it requires one more thing we haven’t talked about nearly as much: a stable, performant, and easy-to-use foundation. Setting up that foundation is the chief goal of the field of machi...

In many ways, 2023 was the year of AI in tech, which is a double-edged sword. On the one hand, the basic technology is straightforwardly exciting — but on the other hand, with seemingly every technology solution scrambling to integrate a thin wrapper around ChatGPT, it’s hard to stand out in a saturated environment. This month on the Klaviyo Data Science Podcast, we dive into a case study of how t...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Equity, Diversity, and Inclusion Equity, diversity, and inclusion (EDI) are more than just central principles of successful teams in data science and beyond — they’re also a rich field that presents interesting and challenging data science problems. This episode, we chat with two EDI specialists at Klaviyo about EDI, the...

2023 Year in Review As the new year starts, we take a look back at 2023. We spoke to 11 data scientist and people who work closely with data scientists, and we asked them all the question we ask every year: what is the coolest data science thing you learned about in 2023? You’ll hear a wide range of answers, including: How data science moving to peripheral devices and becoming more accessible has...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Knowing your customers Customers are all unique, whether you’re building a data science product or selling an ecommerce product. In an ideal world, we’d be able to think about all of them on a truly one-on-one basis. Most of us can’t keep track of that many people in our brains, though, which is where the topic of today’...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… When Things Break Welcome to the November episode of the Klaviyo Data Science Podcast for this year! November is a unique month for ecommerce, which makes it a unique month for any software solution built for ecommerce; it’s a tradition on this podcast to take the opportunity to celebrate some of those unique challenges....

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Off the Happy Path In most discussions about data science and data science features on this podcast, we make a basic, foundational assumption: the users whose data we are thinking about and customer experience we are trying to improve are, generally speaking, trying to use the platform in a way we recognize and approve o...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Presenting your work for fun and profit Presenting technical work is not something you automatically learn how to do — just like the technical skills themselves, it has to be learned and practiced, and opportunities to practice it can be hard to find. This episode, we discuss one opportunity that Klaviyo put together for...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… An introduction to production What comes after you finish building a data science model? If you’re working on a software project, the answer likely involves that model serving customers in production. Understanding production is crucial for any data scientist or software engineer, so we spend this episode learning about...