Klaviyo Data Science Podcast

Klaviyo Data Science Team
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Klaviyo Data Science Podcast

This podcast is intended for all audiences who love data science--veterans and newcomers alike, from any field, we’re all here to learn and grow our data science skills. New episodes monthly. Learn more about Klaviyo at www.klaviyo.com!

  1. Klaviyo Data Science Podcast EP 53 | Yahoogle

    ١٢ جمادى الأولى

    Klaviyo Data Science Podcast EP 53 | Yahoogle

    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: Yahoogle. It’s more than just a funny word! The challenges in operating a large-scale email sending system Advice and retrospectives about large, cross-functional projects and tight deadlines For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

    ٤٨ من الدقائق
  2. Klaviyo Data Science Podcast EP 52 | Our Favorite Models and How They Work

    ٧ ربيع الآخر

    Klaviyo Data Science Podcast EP 52 | Our Favorite Models and How They Work

    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: The benefits of simple models How learning about some models teaches you about entire fields of mathematics  What fitting a classification model has to teach us about epistemology  For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

    ٥١ من الدقائق
  3. Klaviyo Data Science Podcast EP 51 | How Personalization Empowers Users

    ٧ ربيع الأول

    Klaviyo Data Science Podcast EP 51 | How Personalization Empowers Users

    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: Why personalization is a matter of finishing your user’s… sandwiches  How to approach complex personalization features as a data scientist, a designer, or an engineer  What the game of Battleship can teach you about personalization For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

    ٤٤ من الدقائق
  4. Klaviyo Data Science Podcast EP 50 | The 50th Episode Celebration Special

    ٢ صفر

    Klaviyo Data Science Podcast EP 50 | The 50th Episode Celebration Special

    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: Why you don’t need a fancy mic to get started How to approach talking about deep, technical work on the air What we have planned for the next 50 episodes For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠⁠⁠⁠⁠.

    ٤٥ من الدقائق
  5. Klaviyo Data Science Podcast EP 49 | What Real Data Scientists Wish They'd Known Earlier in their Careers

    ١٢ محرم

    Klaviyo Data Science Podcast EP 49 | What Real Data Scientists Wish They'd Known Earlier in their Careers

    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: How taking a more scenic or indirect career route can impart valuable experiences  The growth and development opportunities that truly unlocked new phases of their careers, and how you can make the most of similar opportunities Why mistakes often make the best learning opportunities  For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠⁠⁠⁠.

    ٤٤ من الدقائق
  6. Klaviyo Data Science Podcast EP 48 | Data Science Goes Worldwide

    ٤ ذو الحجة

    Klaviyo Data Science Podcast EP 48 | Data Science Goes Worldwide

    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: How strings can be far more complex than they seem Why changing languages doesn’t just mean changing words  Where in the world your assumptions about language may break For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠⁠⁠.

    ٤٣ من الدقائق
  7. Klaviyo Data Science Podcast EP 47 | Cooking Up Something Special with Data Science: Made In Cookware

    ٢٩ شوال

    Klaviyo Data Science Podcast EP 47 | Cooking Up Something Special with Data Science: Made In Cookware

    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: What kitchen knives can teach you about product design and development Which type of pan you should use to cook a steak, and how that can help you understand customer segmentation How AI saves real marketers real time while also giving them better results 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.

    ٤٨ من الدقائق
  8. Klaviyo Data Science Podcast EP 46 | ML Ops 101

    ٣٠ رمضان

    Klaviyo Data Science Podcast EP 46 | ML Ops 101

    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: How ML Ops is different from the similar fields of data science and DevOps What skills a successful ML Ops developer should have, and what an ML Ops developer’s day-to-day looks like Why concepts like “velocity” and “stability” have their own special nuances in the world of ML Ops For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠⁠.

    ٤٥ من الدقائق
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This podcast is intended for all audiences who love data science--veterans and newcomers alike, from any field, we’re all here to learn and grow our data science skills. New episodes monthly. Learn more about Klaviyo at www.klaviyo.com!

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