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:
For more details, including links to the many resources our panel suggest to learn more about leadership and management, check out the full writeup on Medium!
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:
For the full show notes, including who's who, see the Medium writeup.
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 one you built or fine-tuned and are comparing to state-of-the-art models, maybe one you’re considering loading up and using for a project you’re about to start. If you look at the literature today, you can get a sense of what the average performance for the model is on an evaluation or set of tasks. But often, that’s unfortunately the extent of what it’s possible to learn —there is much less emphasis placed on the variability or uncertainty inherent to those estimates. And as anyone who’s worked with a statistical model in the past can affirm, variability is a huge part of why you might choose to use or discard a model.
This seminar explores how to best compute, summarize, and display estimates of variability for AI models. Listen along to hear about topics like:
About Evan Miller
You may already know our guest Evan Miller from his fantastic blog, which includes his celebrated A/B testing posts, such as “How not to run an A/B test.” You may also have used his A/B testing tools, such as the sample size calculator. Evan currently works as a research scientist at Anthropic.
About Anthropic
Per Anthropic’s website:
You can find more information about Anthropic, including links to their social media accounts, on the company website.
Anthropic is an AI safety and research company based in San Francisco. Our interdisciplinary team has experience across ML, physics, policy, and product. Together, we generate research and create reliable, beneficial AI systems.
Special thanks to Chris Murphy at Klaviyo for organizing this seminar and making this episode possible!
For the full show notes, including who's who, see the Medium writeup.
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:
For the full show notes, including who's who, see the Medium writeup.
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 where you’ve genuinely created something new? Perhaps more importantly, how do you know when you’re in that situation?
This month, we explore one of the best tools to help answer both questions: the patent process. Listen along with your fearless co-hosts and a member of Klaviyo’s legal team to learn about what a patent is, why getting them matters, and how to get your own novel work patented, along with:
For the full show notes, including who's who, see the Medium writeup.
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 platforms make sweeping changes to their spam filters, providing a few short months of notice? Stress, uncertainty, and an opportunity for individuals and organization to rise to the challenge. In this month’s episode, we talk with analysts, engineers, and product managers to discuss Klaviyo’s journey to meet Yahoo and Google’s new Email Delivery Requirements — aka Yahoogle, the colloqial name for a new set of rules that must be followed by email senders to have their emails make it to inboxes and not go straight to the junk bin.
Listen in to hear more about:
For the full show notes, including who's who, see the Medium writeup.
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 the same time, most data scientists would acknowledge that some models manage to stand out. Maybe it’s nostalgia, maybe it’s how powerful they are in some settings, maybe it’s another factor entirely — but for one reason or another, most data scientists will admit they have a soft spot for some models. That’s what we’re here to discuss this month: what is your favorite model? Listen in to hear more about:
For the full show notes, including who's who, see the Medium writeup.
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 team we interviewed this month has tackled. Listen in to hear more about:
For the full show notes, including who's who, see the Medium writeup.
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, and what we’ve learned through the years. You’ll hear about:
For the full show notes, including who's who, see the Medium writeup.
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 are. Listen to this advice-filled episode to hear:
For the full show notes, including who's who, see the Medium writeup.
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 challenges: international growth, i.e. expanding the range of countries and languages your products are natively available in.
This month, we talked with multiple members of the internationalization effort here at Klaviyo, from teams across our organization. You’ll hear about:
For the full show notes, including who's who, see the Medium writeup.
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 solutions is that it actually matters to real people.
This month, we talk to some of those people. In particular, we sat down with two members of the team at Made In Cookware (http://madeincookware.com/) to discuss what makes their business unique, how they approach understanding and marketing to their customers, and how data science and AI help them do all of that. You’ll hear about:
About Made In
Made In Cookware (Made In) is a premium cookware brand based in Austin, TX. Founded in 2017 but born of a 4th-generation, family-owned kitchen supply business, Made In creates best-in-class cookware developed in partnership with the world’s finest chefs and foremost craftsmen. Today, you’ll find Made In products in more than 2,000 restaurants, in the hands of James Beard Award-winning chefs at Michelin-starred restaurants across the country, and in the kitchens of home cooks everywhere. Made In products have garnered over 100,000 5-star reviews, and the company was named one of Inc. Magazine’s best workplaces and Newsweek’s best online shops of 2024.
For the full show notes, including who's who, see the Medium writeup.
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 machine learning operations, aka ML Ops.
This month on the Klaviyo Data Science Podcast, we give a brief but thorough introduction to the field of ML Ops. You’ll hear about:
For the full show notes, including who's who, see the Medium writeup.
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 to build AI products, SegmentsAI, and discuss the principles that go into making sure your AI-powered product shines — and, more importantly, actually helps your customers. You’ll hear about:
“Why do this, why build another LLM feature? It seems like every website is rushing to get their name next to AI... How you break through the noise is to actually provide value to people, not novelty. Being able to help customers speed up or generate new, interesting segments that they otherwise wouldn’t? I think that’s valuable.”— Rob Huselid, Senior Data Scientist
For the full show notes, including who's who, see the Medium writeup.
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 data that powers it, and the challenges that come with using that data. You’ll hear about:
For the full show notes, including who's who, see the Medium writeup.
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:
“You don’t have to have a PhD any longer to do data science. And I think that’s amazing and powerful, and it’s going to mean that the future is… where everybody is allowed to do data science stuff without having lots and lots of education.”
— Wayne Coburn, Director, Product Management
For the full show notes, including stories mentioned in the episode and who's who, see the Medium writeup.
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’s episode comes in: what is the best way to summarize an entire population of customers into a number of groups that is small enough to intuit but fine-grained enough to actually be useful in practice?
Listen along to learn more about:
For the full show notes, including resources mentioned in the episode and who's who, see the Medium writeup.
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.
In an ideal world, software and data science products would never break. We do not live in an ideal world, though, so an important question to answer is: what should you do when things do break? This month, we discuss incidents, incident response, and getting things back on track as quickly and effectively as possible to continue delivering value to your customers.
Listen along to learn more about:
For the full show notes, including resources mentioned in the episode and who's who, see the Medium writeup.
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 of. Not all users of an application have this intention, and the data science behind detecting users who misuse a platform— and even abuse it — constitutes a complex and vast field of study.
Listen along to learn more about:
For the full show notes, including who's who, see the Medium writeup.
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 its R&D teams this summer: the Klaviyo R&D Science Fair. Listen along to hear about:
“We put together a little game: try to find all of the accessibility problems in this form, without using the tool that we built…. And then when they react, ‘oh my God, like that one was impossible, I don’t know how you expected me to find that,’ that’s when we can say: exactly! That’s why we needed this feature!”— Maya Nigrin, Senior Software Engineer
For the full show notes, including photos of the event, see the Medium writeup.
