In this episode, we'll discuss what network effects are, how they introduce challenges in the standard A/B testing framework, and how the cluster experimentation method can be leveraged to address these challenges. We will also delve into the technical details of how clusters can be generated, and evaluated, and the associated trade-offs that need to be considered.

Based on their published tech blog, with the link provided here for your reference: https://medium.com/@AnalyticsAtMeta/how-meta-tests-products-with-strong-network-effects-96003a056c2c

In this episode, we'll explore how the data science team from Grammarly developed their geo-experimentation to measure marketing effectiveness. We will cover about three components in designing an A/B testing experiment, as well as considerations regarding the opportunistic costs of the experimentation.

Based on their published tech blog, with the link provided here for your reference: https://www.grammarly.com/blog/engineering/measuring-marketing-effectiveness-in-a-cookie-less-world/ 

In this episode, we'll explore how the data science team from Shopify leverages Monte Carlo Simulation to develop their sampled success metrics. We'll discuss what is sampled success metrics, the associated trade-offs needed to build them, and how Monte Carlo simulation can be used to inform decisions.

Based on their published tech blog, with the link provided here for your reference: https://shopify.engineering/monte-carlo-simulations-sampled-success-metrics

In this episode, we'll explore how the data science team from Klaviyo developed a Generative AI Product that enhances experiences and enables efficient customer segmentation. We'll also discuss two key concepts in Generative AI: prompt chaining and few-shot learning.

Based on their published tech blog, with the link provided here for your reference: https://klaviyo.tech/building-segments-ai-fe33d9cab822

Description: In this episode, we will talk about the importance of remediating failed workflow jobs to reduce business infrastructure costs. We delve into Netflix's approach, which involves enhancing their existing rule-based error classifier with advanced machine learning models. This allowed for auto-remediation, improving the handling of memory configuration and unclassified errors, ultimately leading to substantial cost savings.

Based on their published tech blog, with the link provided here for your reference: https://netflixtechblog.com/evolving-from-rule-based-classifier-machine-learning-powered-auto-remediation-in-netflix-data-039d5efd115b

In this episode, we will talk about what is technical debt in software engineering and its associated risks. We will also share a set of metrics to measure the status of technical debt and ways to help companies quantify their progress toward better software engineering efforts.

Based on their published tech blog, with the link provided here for your reference: https://medium.com/booking-com-development/measuring-technical-debt-to-avoid-the-boiling-frog-syndrome-c44eb48b3ce1

In this episode, we will discuss how the Science team at Amazon designs pricing experiments to improve experimentation power. We will cover concepts like the carryover effect and spillover effect, as well as the solutions the team developed to overcome those challenges.

Based on their published tech blog, with the link provided here for your reference: https://www.amazon.science/blog/the-science-of-price-experiments-in-the-amazon-store

In this episode, we will talk about what is position bias in recommendation systems, and how the applied scientist team at Uber tackled this challenge.

Based on their published tech blog, with the link provided here for your reference: https://www.uber.com/blog/improving-uber-eats-home-feed-recommendations

In this episode, we will take a look at one analytical approach in decision-making called Analytical Hierarchy Processing (AHP). The New York Times team leverages this AHP process to help with making better collective decisions in their technology choices for privacy.

Based on their published tech blog, with the link provided here for your reference: https://open.nytimes.com/collective-decision-making-with-ahp-3ef819e5bc2a

In this episode, we delve into a study conducted by Intuit on measuring the productivity impact of the Gen AI tool on their data analyst team. It demonstrates exciting positive productivity gains, offering an exciting outlook for many in the industry.

Based on their published tech blog, with the link provided here for your reference: 

​​https://medium.com/intuit-engineering/how-intuit-data-analysts-write-sql-2x-faster-with-internal-genai-tool-c3b9d482208a