Snacks Weekly on Data Science

In this episode, we explore the concept of out-of-app marketing and its importance for businesses like Uber. We'll discuss the challenges involved in personalizing out-of-app marketing messages, the essential components of their recommendation architecture, and how the team created customized solutions to improve personalization and relevance in their recommendations.

For more details, you can refer to their published tech blog, linked here for your reference: https://www.uber.com/blog/personalized-marketing-at-scale/

In this episode, we will discuss the importance of ETA for ridesharing apps and the challenges of providing a reliable ETA to users upfront. We delved into the practices of the machine learning team at Lyft, examining how they developed a solution to address this unique challenge using a lightweight machine learning model.

For more details, you can refer to their published tech blog, linked here for your reference: https://eng.lyft.com/eta-estimated-time-of-arrival-reliability-at-lyft-d4ca2720bda8

In this episode, we will explore how Yelp navigates the challenges of incorporating video reviews into its platform. We will discuss the use of machine learning to detect inappropriate content and the strategies to maintain the quality and integrity of its platform.

For more details, you can refer to their published tech blog, linked here for your reference:
https://engineeringblog.yelp.com/2024/03/moderating-inappropriate-video-content-at-yelp.html

In this episode, we will introduce what a brand is and why measuring brand perception is crucial. We will discuss how social media data can be used to achieve this and look at how Airbnb leverages deep learning methods and word embedding technologies to quantify brand perception more effectively.

For more details, you can refer to their published tech blog, linked here for your reference:
https://medium.com/airbnb-engineering/airbnb-brandometer-powering-brand-perception-measurement-on-social-media-data-with-ai-c83019408051

In this episode, we will explore the concept of quasi-experimentation and its role in Instacart's decision-making process. We will take a closer look at one specific methodology, regression discontinuity design, explaining its key concepts and demonstrating how it works through an interesting example.

For more details, you can refer to their published tech blog, linked here for your reference: https://tech.instacart.com/optimizing-at-the-edge-using-regression-discontinuity-designs-to-power-decision-making-51e296615046

In this episode, we will discuss the machine learning architecture built by LinkedIn for their premium product recommendation. We will explore the machine learning architecture, which includes a two-towered neural network and reinforcement learning as key components.

For more details, you can refer to their published tech blog, linked here for your reference: https://www.linkedin.com/blog/engineering/machine-learning/matching-linkedin-members-with-the-right-premium-products

In this episode, we will discuss why search relevance is important for Faire, how their data team quantifies semantic relevance in search, and how they leverage large language models (LLMs) to measure it efficiently.

For more details, you can refer to their published tech blog, linked here for your reference:
https://craft.faire.com/fine-tuning-llama3-to-measure-semantic-relevance-in-search-86a7b13c24ea

In this episode, we will touch on the importance of A/B testing in the product decision-making process. We will share the four types of metrics and the decision-making framework used by the Data Science team at Spotify, as well as the necessary statistical adjustments that need to be incorporated into experimentation to ensure a solid statistical foundation.

For more details, you can refer to their published tech blog, linked here for your reference:
https://engineering.atspotify.com/2024/03/risk-aware-product-decisions-in-a-b-tests-with-multiple-metrics/ 

In this episode, we will discuss the importance of Estimated Time of Arrival (ETA) for DoorDash and how the company enhanced its machine learning model through three key directions: upgrading from a tree-based model to a deep-learning architecture, adopting a multi-task modeling approach, and leveraging probabilistic models.

For more details, you can refer to their published tech blog, linked here for your reference: https://doordash.engineering/2024/03/12/improving-etas-with-multi-task-models-deep-learning-and-probabilistic-forecasts/

In this episode, we will discuss the intricacy of balancing both the art and science aspects in forecasting. We will explore this through two key aspects: validation of forecasting and integrating product impact into forecasting, where combining the art and science can be crucial to enhancing forecasting performance.

For more details, you can refer to their published tech blog, linked here for your reference:
https://medium.com/@AnalyticsAtMeta/forecasting-meta-balancing-art-and-science-92526e1ae36c