Principal Applied Scientist

Job description

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.  

Our team focuses on understanding and predicting how the user interacts with the ads on the search results page. The probability that a user will click on an ad is one of the most critical inputs used in ranking the ads. Similarly, the probability of interacting with the advertiser’s page is important for measuring advertiser and user satisfaction. This particular position is for the modeling team, which builds machine learnt models for predicting such events. The team looks at all aspects of modeling including training data, features, the actual model (DNNs, linear models, decision trees etc) and offline and online evaluation of those models.  

Roles and Responsibilities A successful candidate should be passionate about machine learning and data mining at web scale. They 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. The candidate should also have excellent communication, collaboration and analytical skills.  

Skills & Qualifications  

1. Outstanding expertise and research experience on statistical machine learning, data mining, optimization and Bayesian inference.

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.  

Experience Required

1) PhD or MS degree in CS/EE or related areas is required.  

2) 5+ years’ experience in engineering or data science/data modeling.

3) Familiarity with distributed data processing/analysis and modeling paradigm, such as Map-Reduce and MPI, is preferred.  

4) Great design and problem solving skills, with a strong bias for quality and engineering excellence at scale.

5) 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.    

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request to

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