myGwork - LGBTQ+ professionals & allies
Applied Scientist, AWS Marketing AI/ML
myGwork - LGBTQ+ professionals & allies, Seattle, Washington, us, 98127
Description
Please make an application promptly if you are a good match for this role due to high levels of interest.Amazon Web Services (AWS) is building a world-class marketing organization, and we are looking for an experienced Applied Scientist to join the central data and science organization for AWS Marketing. You will lead AWS personalization, targeting, and lead prioritization related AI/ML products and initiatives, and own mechanisms to raise the science and measurement standard. You will work with scientists, economists, and engineers within the team, and partner with product and business teams across AWS Marketing to build the next generation marketing generative AI and machine learning capabilities directly leading to improvements in our key performance metrics.A successful candidate has an entrepreneurial spirit and wants to make a big impact on AWS growth. You will develop strong working relationships and thrive in a collaborative team environment. You will work closely with business leaders, scientists, and engineers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. The ideal candidate will have experience with machine learning architectures and models. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment. You will work on high-impact, high-visibility products, with your work improving the experience of AWS leads and customers.Key job responsibilitiesLead the design, development, deployment, and innovation of advanced science models in the strategic area of marketing measurement and optimization.Partner with scientists, economists, engineers, and product leaders to break down complex business problems into science approaches.Understand and mine the large amount of data, prototype and implement new learning algorithms and prediction techniques to improve long-term causal estimation approaches.Design, build, and deploy effective and innovative ML solutions to improve components of our ML and causal inference pipelines.Publish and present your work at internal and external scientific venues in the fields of ML and causal inference.Influence long-term science initiatives and mentor other scientists across AWS.Basic QualificationsPhD, or Master's degree and 4+ years of CS, CE, ML or related field experience3+ years of building models for business application experienceExperience in patents or publications at top-tier peer-reviewed conferences or journalsExperience programming in Java, C++, Python or related languageExperience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computingPreferred QualificationsExperience using Unix/LinuxExperience in professional software developmentKnowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members
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Please make an application promptly if you are a good match for this role due to high levels of interest.Amazon Web Services (AWS) is building a world-class marketing organization, and we are looking for an experienced Applied Scientist to join the central data and science organization for AWS Marketing. You will lead AWS personalization, targeting, and lead prioritization related AI/ML products and initiatives, and own mechanisms to raise the science and measurement standard. You will work with scientists, economists, and engineers within the team, and partner with product and business teams across AWS Marketing to build the next generation marketing generative AI and machine learning capabilities directly leading to improvements in our key performance metrics.A successful candidate has an entrepreneurial spirit and wants to make a big impact on AWS growth. You will develop strong working relationships and thrive in a collaborative team environment. You will work closely with business leaders, scientists, and engineers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. The ideal candidate will have experience with machine learning architectures and models. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment. You will work on high-impact, high-visibility products, with your work improving the experience of AWS leads and customers.Key job responsibilitiesLead the design, development, deployment, and innovation of advanced science models in the strategic area of marketing measurement and optimization.Partner with scientists, economists, engineers, and product leaders to break down complex business problems into science approaches.Understand and mine the large amount of data, prototype and implement new learning algorithms and prediction techniques to improve long-term causal estimation approaches.Design, build, and deploy effective and innovative ML solutions to improve components of our ML and causal inference pipelines.Publish and present your work at internal and external scientific venues in the fields of ML and causal inference.Influence long-term science initiatives and mentor other scientists across AWS.Basic QualificationsPhD, or Master's degree and 4+ years of CS, CE, ML or related field experience3+ years of building models for business application experienceExperience in patents or publications at top-tier peer-reviewed conferences or journalsExperience programming in Java, C++, Python or related languageExperience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computingPreferred QualificationsExperience using Unix/LinuxExperience in professional software developmentKnowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members
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