Klaviyo Data Science Podcast – Details, episodes & analysis

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.

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.

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:

• Different types of human behaviors motivating platform misuse, and how that translates to different types of data
• What makes many-to-one problems so challenging
• Why keywords alone are not enough

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:

• How, much like software development, explaining technical work is an iterative process
• The best ways to engage a crowd and get them interested in what you have to say
• The unique and powerful allure of scissors and glue guns

“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.

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 best practices from three experienced Klaviyo engineers.

Listen along to learn more about:

• How to make sure your code is “battle-ready,” whether you’re working on a data science project or not
• Why error messages you think are safe to ignore may not actually be safe to ignore
• One key lesson for safely deploying your code, no matter what environment you work in

“That’s stuck with me through the years: there are these knock-on effects between things. Even if it’s not your code, you should still try to understand how it’s working and whether it can have a ripple effect that comes back and affects your code.”— Chris Conlon, Lead Software Engineer

Check out the full show notes on Medium!

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 research they did before joining the team — both data science research and other kinds — to see what we can learn about conducting effective data science research.

Listen along to learn more about:

• What tiny iron meteorites teach us about the importance of using your results to tell a compelling story
• What data science research into commerce and policy teaches us about iterating on your research questions
• What rubber beams teach us about the importance of getting feedback early

“Everybody has a unique perspective could be the one that opens up a brand new door. You’re looking at doing specific algorithms, you’re looking at doing the research a specific way, but there could be an alternative path.” 

- Mike Galli, Data Scientist

See the full writeup on Medium!

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 everything that follows. Meaning: if you’re going to change the home page, you need to really know what you’re doing.

This month, we talk with the Klaviyo engineering team that did just that. We discuss many aspects of that redesign, including:

• How to get buy-in from teams you depend on without taking away your own independence
• The unique difficulties that come with large front-end engineering projects and smart data visualization
• How to filter through the noise when evaluating the success of a feature

“There are very few features ever been released in Klaviyo that have seen that sort of change… At the end of the day, if we can help our users complete tasks faster and more effectively, that’s our highest priority.”
- Griffin Drigotas, Senior Product Designer

See the full writeup on Medium!

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 classically trained as a data scientist, but as something else. This month, we examine that process: the process of working in a field that’s distinct from data science and becoming a data scientist.

We discuss several parts of that journey, including:

• What attracts someone to data science in the first place
• How to approach gaining the technical skills you need to get a data science job
• How similar some parts of the data scientist job are to washing dishes

Where do data scientists come from?“You really need to practice using these tools. I did my best to come up with excuses to use data science techniques in all my projects… maybe instead of trying to automate a workflow in Excel VBA, I’d try to automate it in python instead.”
- Steven Her, Data Scientist

Read the full writeup on Medium!

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 answers. Listen on if you’ve wanted to know more ways to learn about:

• How to think about and employ the Bayesian framework (and corgis)
• Learning intro-to-intermediate coding skills necessary for data science work
• The theory that drives natural language processing
• The mindset of a data scientist in general

“it gives you a different lens to apply to different problems. And sometimes taking that different lens, suddenly a problem that was really hard to formulate using traditional frequentist statistics or machine learning techniques, suddenly it can be really easy to frame in this other way” - Tommy Blanchard, Senior Data Science Manager

Read the full writeup on Medium!

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…

We’ve talked about a lot of aspects of data science on this podcast — building software features, conducting research, learning new methods and skills, recruiting new members — but there’s one we’ve always avoided: building a new team from the ground up. A large reason for that is personnel — while your cohosts may be intrepid, they are not experts in this area.

This month, we bring on two people who are: Eric Silberstein and Ezra Freedman, who founded the Data Science team at Klaviyo. We draw on their wealth of experience, knowledge, and lessons learned the hard way while founding a young team.

As you might expect, these lessons extend beyond data science teams in particular — whether you’re founding another team or starting a new business, or looking to join a team in its early stages, you might be able to learn from our discussions, such as:

• How setting concrete goals is key for a new team
• How to think about your first hire, and your next five
• How to steer a team through large organizational changes while maintaining its culture and essence

Read the full writeup on Medium!