Senior Applied Scientist - Advertiser Optimization

Overview
Ads monetization team part of the Web Experiences Team (WebXT) org is looking for a Senior Applied Scientist, Advertiser Optimization in Redmond, WA or Mountain View, CA.
Our team focuses on optimizing ads performance for all advertisers in Microsoft Advertising by bidding on their behalf into real time auctions across our marketplaces. This is done through algorithm development, optimization, and experimental analysis of advertiser strategies and auction mechanisms.
In our team, engineers and scientists work together and utilize all sorts of platforms, techniques, and approaches, including but not limited to mathematical modeling and optimization, machine learning, optimal control, and general operation research.
We build and develop both online stacks as well as offline workflows to support our algorithm.
At its core, our team utilizes signals of user and advertiser intent as well as auction's characteristic to determine in real-time or near-real-time which ads can enter the auctions and their value. Our work directly impacts billions of dollars in revenue annually.
We are looking for a skilled Senior Applied Scientist with development skills and a background in quantitative fields such as statistical machine learning, decision theory, operation research, optimization theory, mathematical modeling, data mining, causal inference, information retrieval, game theory, mechanism design, optimal control.
Responsibilities

• Drive algorithmic improvements to online and offline systems, developing and delivering robust and scalable solutions, making direct impacts on advertisers' experience, and continually increasing the revenue for Bing Ads.
• Design, implement, and analyz bidding strategies using techniques from optimization, control theory.
• Design and oversee large-scale, long-term experiments to improve the health of the marketplace using advanced statistics and machine learning.
• Design automation algorithms for advertisers using techniques from AI and ML to improve advertisers' return on investment.
• Develop models for causal reasoning using techniques from AI, ML, and statistics.
• Debug model performance and prediction at scale to understand and answer customer questions.

Qualifications
Required/Minimum Qualifications

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research)
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.

Additional or Preferred Qualifications

• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• 3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers)
  in the following areas is preferred statistical machine learning, deep learning, data mining, causal inference, information retrieval, game theory, mechanism design, optimization, and optimal control.
  3+ years experience conducting research as part of a research program (in academic or industry settings).
• 1+ year(s) experience developing and deploying live production systems, as part of a product team.
• 5 years of hands-on experience in applying any of the following areas in the industry: statistical machine learning, deep learning, data mining, causal inference, information retrieval, game theory, mechanism design, optimization, and Bayesian inference.
• 3+ years of experience with computational advertising
• 2+ years of experience in Automated Bidding or Budget Pacing.

Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $112,000 - $218,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $145,800 - $238,600 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay
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. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
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