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Microsoft Senior Applied Scientist China, Beijing, Beijing 306593678
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Overview
Microsoft is innovating rapidly in advertising to grow its share of this market by providing the advertising industry with the state-of-the-art online advertising platform and service. Bing Ads Algorithm Team is at the core of this effort, working on the following research & development: User Response (click & conversion) Prediction; Machine learning (ML); Natural Language Processing (NLP) and Information Retrieval (IR). We heavily use the recent advances in cloud computing infrastructure to harness huge volume of data for solving many of the above-mentioned problems. We love big data! The team is a world-class R&D team of passionate and talented scientists and engineers who aspire to solve challenging problems and turn innovative ideas into high-quality products and services that can help hundreds of millions of users and advertisers, and directly impact our business. We are hiring Senior Applied Scientist.
Qualifications
Good design and problem-solving skills, with a bias for quality and engineering excellence at scale. The most successful candidates will possess the ability to learn new techniques from textbooks or research papers and apply them to the business problem at hand. Experience on natural language processing/understanding, large scale model training system, information retrieval, user response (click & conversion) prediction and auction mechanism is a good addition.
Requirement
M.S. or PhD degree in CS/EE or related areas is required.
Responsibilities
The primary responsibility for this position is developing machine learning models in conversion prediction system. A good candidate will play a key role in driving algorithmic and modeling improvement to the system, analyzing performance and identifying opportunities based on offline and online experiments, developing and delivering robust and scalable solutions, making direct impact to both user and advertisers experience, and continually increasing the revenue for Bing ads.
- Machine learning
Improve machine learning models for user response prediction; Advance feature engineering and experimentation; Data/feature analysis/design for agile scientific experimentation.
- Natural Language Processing
Develop semantic models/features with multilingual pretrained language models for response prediction; Improve user profile with text classification.
- Big data
To research and implement algorithms based on big data, lead the research and innovation in machine learning, tap the business potential using technology to analyze customer behavior and advance product experience.
The candidate should also have good skills on communication, collaboration and analytics.
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