Klaviyo Data Science Podcast Klaviyo Data Science Team

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!