Data & Applied Scientist II

The Product Ads Algorithm & Infrastructure team in Ads Understanding team is hiring an Data & Applied Scientist II for our Vancouver, British Columbia, Canada location. The team is responsible for product ads selection, relevance, modeling, and online infrastructure for serving and experimenting cutting edge algorithms, ranging from natural language processing (NLP) to information retrieval, computer vision, etc. The team leverages heavily on deep learning methodologies to build solutions to meet the Commerce Strategy of Microsoft, where Product Ads is at the center of it! We are looking for a passionate scientist to deliver an amazing shopping experience with our multi-modal models backing it up.   

Online Advertising is one of the fastest growing businesses on the Internet today, with about $70 billions of a $600 billion advertising market already online. Search engines, web publishers, major ad networks, and ad exchanges are now serving billions of ad impressions per day and generating terabytes of user events data every day. Rapid online advertising growth has created enormous opportunities and technical challenges that demand computational intelligence. Computational Advertising has emerged as a new interdisciplinary field that involves information retrieval, machine learning, data mining, statistics, operations research, and micro-economics, to solve challenging problems that arise in online advertising. The central problem of computational advertising is to select an optimized slate of eligible ads for a user to maximize a total utility function that captures the expected revenue, user experience and return on investment for advertisers. Microsoft is innovating rapidly in this space to grow its share of this market by providing the advertising industry with the state-of-the-art online advertising platform and service. Microsoft Ads Relevance and Revenue (RnR) team is at the core of this effort, responsible for research & development of all the algorithmic components in our advertising technology stack, including,  

• User/query intent (text and image) understanding, document/ad understanding, user targeting - Relevance modeling, IR-based ad retrieval  
• User response (click & conversion) prediction using large scale machine learning algorithms - Marketplace mechanism design and optimization, and whole-page experience optimization - Personalization  
• Innovative new ads products  
• Network protection, fraud detection, traffic quality measurement  
• Advertising metrics and measurement, including relevance and ad campaign effectiveness  
• Data mining and analytics  
• Supply-demand forecasting  
• Ad campaign planning and optimization  
• Experimentation infrastructure including tools for configuring and launching experiments, dashboard, live marketplace monitoring, and diagnosis.  

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