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 l…

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 top…

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 …

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? Y…

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 creatin…

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 diffe…

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 th…

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 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…

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 know…

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 ty…

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 spen…

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 f…

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 S…

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 …

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 pe…

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 …

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 opportu…

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 us…

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 dis…

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 …

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Research is a core part of data science. But data science is far from alone in that respect — other fields rely on research just as heavily, and they have their own set of hypotheses, methods, complications, and concerns. This month, we talk to three Klaviyos about resear…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Few parts of your product, application, or webpage are more crucial than the very initial experience. In a web application like Klaviyo, that means the home page. Everyone sees it every time they log on to do anything, and interactions with that page set the tone for ever…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… The question is slightly tongue-in-cheek, but only slightly. Data science is a new field — while many people today are graduating with degrees in data science, the same was not true a decade ago. Many of the people who work (and will work) as data scientists were not clas…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Back by popular demand: data science is a broad, deep field with an extraordinary amount to learn, and we’re here to help you learn it. We asked four members of the Data Science team at Klaviyo what one of their favorite data science books was, and we got four different a…

Listen to the full episode on Anchor, or in your favorite podcast distribution platform! Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Starting from scratch We’ve talked about a lot of aspects of data science on this podcast — building software features, conducting research, learning new methods and skills, recruitin…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… When the data science world changes When you work in data science, it’s inevitable that the world will change for you. Sometimes it’s due to global events, macroeconomic trends, or sudden shifts in consumer behavior. Other times it’s due to new features added by a commonl…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… 2022 Year in Review As the new year starts, we take a look back at 2022. We spoke to 8 data scientist and people who work closely with data scientists, and we asked them all the same question: what is the coolest data science thing you learned about in 2022? You’ll hear a…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Tools of the Trade We talk a lot on this podcast about the results of data science and software engineering work. We even talk about the process of doing data science and software engineering work. But one thing we haven’t shed much light on, until this month, is: what sp…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Anomaly Detection It’s our third November on the Klaviyo Data Science Podcast, and if you work in ecommerce, you know that November means one thing: Black Friday and (usually) Cyber Monday, i.e. the month of the year where everything changes. Traditionally, we’ve talked a…

I’ll let you in on a secret: this podcast does not cover everything. We cover a wide array of projects, go into detail on a variety of aspects of them, and speak to a diverse panel of data scientists and people related to the data science world, but we still can’t cover everything. This month, to give you a taste of what we haven’t been able to sho…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Using NLP to communicate at scale Last episode, we discussed the history and practice of natural language processing, or NLP. This month, we’re here to discuss an exciting and cutting-edge application: using NLP to help businesses converse with their customers at scale. S…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… What’s the deal with natural language? Natural language processing, or NLP, is one of the dominant forces in modern data science, and it’s produced a host of data science-powered products many people take for granted as a basic fact of life. It hasn’t always been so power…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Thinking big-picture with A/B testing We’ve discussed A/B testing multiple times on this podcast, for good reason. But there’s an important angle we have yet to cover: in the life of a researcher or marketer, there’s no such thing as an A/B test. There’s an entire system …

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Using data science to help people write Using machine learning models to generate text, images, and other creative objects is, as they say, a bit of a hot topic right now. There are examples of models like this in action all across the internet and across different fields…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Writing code for computers and people No matter what sort of data science work you do, it’s fairly inevitable that you’ll have to write code to accomplish your goals. For substantial projects, it’s also fairly inevitable that you’ll have to work with other people to see t…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… What are data privacy and security? Data privacy and security are huge and hugely important topics — in all likelihood, you already know a little about them if you’re reading this intro. But they are both crucial to any good data science work, and this month we explore th…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Customer-focused research This month, we focus on research — but specifically research that’s aimed at your customers, delivering the sort of insight they would try to glean by running experiments and analysis using their own data. In particular, we dive into two differen…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Customer research: your secret weapon You can study as much mathematical theory, invent as sophisticated a machine learning model, or write as clean production-ready code as you want — if you don’t make sure you’re solving the right problems to begin with, all that effort…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Fuel for the Creative Fire It’s no secret: being creative is hard. Creativity requires time and energy, at the bare minimum, and lacking creativity can spiral into writer’s block and other such conditions. That may be okay if you’re just sending out a tweet here or there …

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Slow Problems, Quick Solutions We’ve devoted quite a bit of time on this podcast to robust, carefully tuned, and vetted-in-a-thousand-ways solutions. This episode, we venture beyond the land of neatly trimmed hedges and into the unknown, where scrappy solutions may be the…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Solving difficult problems with data science This month, we talk with Shane Suazo, the founder of Plytrix Analytics, about using data science to drive efficient business growth. Shane and Plytrix work with Vital Proteins, and we dive deep into their story and highlight th…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… (More) required reading for data science A question we frequently get asked is: what books should I read to be a better data scientist/machine learning engineer? This may not surprise you, but there isn’t just one answer — in fact, we spent an entire episode talking about…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Getting real value from data science This week, we talk with Ben Knox from Super Coffee and Gina Perrelli from Lunar Solar Group about using data science to motivate the growth of a business. No hypothetical business cases this week — Super Coffee is a real business with …

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Making your product experiments count We’ve talked about quite a few aspects of data science on this podcast, but one that’s perhaps conspicuously absent so far is running experiments on your product. It’s no secret that experiments provide extraordinarily high-quality da…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Recruiting for a data science team Most of us reading this writeup have probably had at least one interaction with a recruiter. Most of us reading this writeup probably don’t have a deep knowledge of recruiting — what recruiters do, how they help teams scale, and what the…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Required reading for data science A question we frequently get asked is: what books should I read to be a better data scientist/machine learning engineer? This may not surprise you, but there isn’t just one answer — depending on the skills you have, your knowledge base, t…

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Understanding your Customer Lifetime Value This is a math-heavier episode than usual — we’re going to dive into probabilistic distributions and talk about systems of estimators. Even if that’s not your background, though, you should still find this episode useful. That di…

Benchmarks: what are they and why? You’ve probably heard of benchmarks. You’ve probably even used them. But what exactly are benchmarks, how are they useful, and how can you go about building a system to make benchmarks in your own industry? You’ll hear about all that and more, including: How to use benchmarks to make informed decisions about impro…