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United States of America; US - United States of America
Source: Yahoo careers · View original posting
From Yahoo's posting. “We” and “our” refer to the employer.
It takes powerful technology to connect our brands and partners with an audience of hundreds of millions of people. Whether you’re looking to write mobile app code, engineer the servers behind our massive ad tech stacks, or develop algorithms to help us process trillions of data points a day, what you do here will have a huge impact on our business—and the world.
A Little About Us
The Yahoo homepage is one of the most visited destinations on the internet. Our Yahoo News Group Analytics Team is the engine that powers its evolution, using rigorous experimentation and advanced analytics to improve the daily habits of millions of users. We are a high-autonomy team that values statistical integrity, clear communication, and an AI-forward approach to solving complex product problems.
We're hiring a Senior Data Scientist to lead product analytics and experimentation for the Yahoo.com homepage. In this role, you will define how success is measured, design high-stakes experiments, and generate insights that directly shape our product roadmap. You'll partner closely with Product, Engineering, Design, and Data Engineering to guide high-impact decisions. Your work will span the full analytics lifecycle - from metric definition and instrumentation to experimentation and deep-dive analysis.
This is a hands-on senior individual contributor role with significant ownership. You will operate as the analytical lead for your area, bringing structure to highly ambiguous problems and ensuring decisions are grounded in rigorous analysis. The person best suited for this role will be comfortable with vague metrics and energized by ambiguous problems. You've owned experimentation programs end-to-end — not just contributed to them — and you know the difference between a statistically significant result and a meaningful one.
You treat AI tools as a core part of your workflow, not an add-on, and you're always looking for ways to work more efficiently without sacrificing rigor.
Note: This role is focused on product analytics and statistical modeling. It is not an ML engineering role—you will not be responsible for building or productionizing machine learning systems.
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