Principal Applied Science Lead
 Microsoft | |
 United States, Washington, Redmond | |
 Nov 20, 2024 | |
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OverviewOur 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. Our work directly impacts billions of dollars in revenue annually. We are looking for a Principal Applied Science Lead to join our team. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
ResponsibilitiesWe are looking for a Principal Applied Science Lead 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. They will play a key role in leading and driving 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 Microsoft Advertising. Examples of what you may involve in: Designing, implementing, and analyzing bidding strategies using techniques from optimization, control theory. Designing and overseeing large-scale, long-term experiments to improve the health of the marketplace using advanced statistics and machine learning. Designing automation algorithms for advertisers using techniques from AI and ML to improve advertisers' return on investment. Developing models for causal reasoning using techniques from AI, ML, and statistics. Building data pipelines and warehousing solutions to support rapid experimentation, robust metric dashboards, and quick insights and diagnostics. | |