Member & Customer
Member & Customer
Whether it is designing a true-north metric, experimenting to understand the causal impact of a new feature, or developing an in-depth analysis to shape our product strategy, it is our job to maximize the value of data towards building the best products that our members and customers love.
Spreading the Love in the LinkedIn Feed with Creator-Side Optimization
Learn how we discovered some growing pains for both creators and viewers in the feed, how we solved the problems with a smarter feed relevance model, and how we combined multiple experimental techniques to understand the impact of the changes on the whole interconnected feed ecosystem.
Driving Business Decisions Using Data Science and Machine Learning
In a tutorial at the 2019 Strata Data Conference in San Francisco, we shared our experiences and success in leveraging emerging techniques to power intelligent decisions that lead to impactful outcomes at LinkedIn. Here’s a recap.
Optimizing New User Experience in Online Services
"Well begun is half done." This proverb is especially true for a web product when it comes to creating a delightful and proactive user experience. This article describes our work in the last few years optimizing new user experience at LinkedIn, driven by application of data science and advanced analytical methods.
Getting to Know Divyakumar Menghani
Divyakumar Menghani is a Data Science Manager on the Hiring Marketplace Data Science team, where he works on Talent Solution products to help connect companies to talent, and people with jobs.
Data + Intuition: A Hybrid Approach to Developing Product North Star Metrics
From WWW 2017 Companion: "You make what you measure" is a familiar mantra at data-driven companies. Accordingly, companies must be careful to choose North Star metrics that create a better product.
How Notification Queues Contain Hidden Natural Experiments
Randomized experiments, or A/B tests is not always feasible in practice. We identify a natural experiment on the LinkedIn platform based on the order of notification queues to estimate the causal peer effect of a received message on the engagement of a message recipient (based on work anniversary announcements distributed to a member’s connections).