Senior Applied Scientist- Bing Search Ads Understanding
Microsoft
Senior Applied Scientist- Bing Search Ads Understanding
Beijing, China
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Overview
The R&D of Search Ads aims to build an online advertising ecosystem of users, advertisers, and the search engine.
Bing Search Ads Understanding team is chartered to deliver world class algorithm using web scale data. Our mission is to drive user satisfaction, advertiser ROI and Bing revenue. A core challenge is to match advertisers' "Ad display" and users' "query" by build an intelligent system to really understand the users need. This is a very hard problem that demands the most advanced AI models and sophisticated engineering systems. Join us to work on projects highly strategic to Bing search in a fun and fast-paced environment!
Qualifications
• 4+ years of experience in data science, machine learning, Natural Language Processing, or similar areas.
• Strong communications skills (both verbal and written in English).
• Great intuition and ability to “make sense of the numbers.”
• Motivated and self-directed. Demonstrated ability to come up-to-speed quickly on a new technical domain is required.
• Work well in a team environment.
• Knowledge and experience in any of the following areas is strong plus:
• Programming in C/C++/C#.
• Information Retrieval, Recommendation Algo & System, Data Mining, Small/Large Language Models, Natural Language Processing, Machine Learning.
• Strong understanding of the search/Ads ecosystem and marketplace along with technology, experience writing code in production systems at large scale.
Responsibilities
• Partner with our Research and PM team to design, develop and ship innovative algorithms and high-quality features to Search Ads system.
• Develop a deep understanding of search ads products, apply machine learning, statistic data analysis, computational linguistics, and other technologies to identify areas from web-scale data for major improvements.
• Apply SOTA deep learning algorithms and other cutting-edge technologies to build effective and efficient models to improve recall, relevance, and revenue.