SR. MACHINE LEARNING SCIENTIST
Online Advertising is one of the fastest growing businesses on the Internet today, with about $70 billion 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. The rapid growth of online advertising has created enormous opportunities as well as 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. Bing 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 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.
We heavily use the recent advances in grid or cloud computing infrastructure to harness huge volume of data for solving many of the above mentioned problems. We love big data!
The RnR 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. Our experimentation infrastructure allows us to innovate and test new algorithms rapidly with live traffic to measure their effectiveness, and launch them in production as soon as they produce positive results, which makes our work environment productive and rewarding.
The Ads Understanding and Selection team in Bing Ads is responsible for developing scalable analytics solutions and systems in the areas of automatic advertiser campaign generation, effective user targeting, personalized ads selection, ad content and format optimization. The team also works on a number of new initiatives to improve advertising campaign efficiency and advertiser on-boarding experience, as well as technologies underling novel marketing offerings to leverage the synergy between search and display advertising capabilities.
Roles & Responsibilities:
We are looking for a Sr. applied scientist with deep R&D background to conduct research and development on intelligent search advertising system to mine and learn actionable insights from large scale data and signals we collect from user queries and online activities, advertiser created campaigns and their performances, and myriad responses from the parties touched by the system in Bing ads paid search ecosystem. The person will play a key role to drive algorithmic and modeling improvement to the system, analyze performance and identify opportunities based on offline and online testing, develop and deliver robust and scalable solutions, make direct impact to both user and advertisers experience, and continually increase the revenue for Bing ads.
Skills & Qualificactions:
1. Outstanding expertise and research experience on statistical machine learning, data mining, information retrieval, optimization, Bayesian inference and MCMC.
2. Excellent problem solving and data analysis skills.
3. Passionate, self-motivated.
4. Effective communication skills, both verbal and written.
5. Strong software design and development skills/experience.
6. PhD degree in CS/EE or related areas is required.
7. Familiarity with distributed data processing/analysis and modeling paradigm, such as Map-Reduce and MPI, is preferred.
8. Experience in online advertising is a plus.
Online Advertising is one of the fastest growing businesses on the Internet today, with about $70 billion 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. The rapid growth of online advertising has created enormous opportunities as well as 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. Bing 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 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.
We heavily use the recent advances in grid or cloud computing infrastructure to harness huge volume of data for solving many of the above mentioned problems. We love big data!
The RnR 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. Our experimentation infrastructure allows us to innovate and test new algorithms rapidly with live traffic to measure their effectiveness, and launch them in production as soon as they produce positive results, which makes our work environment productive and rewarding.
The Ads Understanding and Selection team in Bing Ads is responsible for developing scalable analytics solutions and systems in the areas of automatic advertiser campaign generation, effective user targeting, personalized ads selection, ad content and format optimization. The team also works on a number of new initiatives to improve advertising campaign efficiency and advertiser on-boarding experience, as well as technologies underling novel marketing offerings to leverage the synergy between search and display advertising capabilities.
Roles & Responsibilities:
We are looking for a Sr. applied scientist with deep R&D background to conduct research and development on intelligent search advertising system to mine and learn actionable insights from large scale data and signals we collect from user queries and online activities, advertiser created campaigns and their performances, and myriad responses from the parties touched by the system in Bing ads paid search ecosystem. The person will play a key role to drive algorithmic and modeling improvement to the system, analyze performance and identify opportunities based on offline and online testing, develop and deliver robust and scalable solutions, make direct impact to both user and advertisers experience, and continually increase the revenue for Bing ads.
Skills & Qualificactions:
1. Outstanding expertise and research experience on statistical machine learning, data mining, information retrieval, optimization, Bayesian inference and MCMC.
2. Excellent problem solving and data analysis skills.
3. Passionate, self-motivated.
4. Effective communication skills, both verbal and written.
5. Strong software design and development skills/experience.
6. PhD degree in CS/EE or related areas is required.
7. Familiarity with distributed data processing/analysis and modeling paradigm, such as Map-Reduce and MPI, is preferred.
8. Experience in online advertising is a plus.
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