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محتوای ارائه شده توسط Klaviyo Data Science Team. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Klaviyo Data Science Team یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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Klaviyo Data Science Podcast EP 33 | How to found a (data science) team

57:38
 
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Manage episode 357245766 series 3251385
محتوای ارائه شده توسط Klaviyo Data Science Team. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Klaviyo Data Science Team یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

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, 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
“When you view the world, do you think of it as ‘if-then’ statements, or do you tend to think of it as some sort of function to optimize? Our team needed both.”
- Eric Silberstein, VP of Data Science

Read the full writeup on Medium!

  continue reading

47 قسمت

Artwork
iconاشتراک گذاری
 
Manage episode 357245766 series 3251385
محتوای ارائه شده توسط Klaviyo Data Science Team. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Klaviyo Data Science Team یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

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, 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
“When you view the world, do you think of it as ‘if-then’ statements, or do you tend to think of it as some sort of function to optimize? Our team needed both.”
- Eric Silberstein, VP of Data Science

Read the full writeup on Medium!

  continue reading

47 قسمت

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