ADCI
Data Science, Applied Science, consumer engagement
AppliedScientist,MAPLE-RecommenderSystem
Neural analysis suggests this role is
optimal for Mid candidates.
“Applied Scientist, MAPLE - Recommender System at ADCI. Skills: Recommender Systems, Machine Learning, GenAI. Participate in model development. Develop new signals”
What You'll Achieve.
Develop personalized experiences; Improve recommendation models; Improve customer experience
Industry & Context.
Solve hard science problems
What They're Looking For.
Must Have
3+ years building models, 4+ years CS, CE, ML experience, Programming in Java, C++, Python, Algorithms and data structures, Parsing, Numerical optimization, Data mining, Parallel and distributed computing, High-performance computing
Nice to Have
Experience using Unix/Linux, Professional software development
What You'll Do.
Participate in model development
Use supervised learning algorithms
Use uplift learning algorithms
Contribute to production code
Contribute to science tooling
Conduct statistical analysis
Work with distributed algorithms
Harness data at scale
Work closely with stakeholders
Present science research
Publish science research
Mentor junior engineers
Mentor junior scientists
How You'll Work.
Team & Collaboration
Internal stakeholders; Business teams; Engineering teams; Partner teams; Science community
Communication Scope
Present research; Publish research
Full Job Description
Are you excited by the idea of developing personalized experiences for Amazon customers as they shop? Are you looking for new challenges and to solve hard science problems while applying state-of-the-art recommendation system modeling and GenAI techniques? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science by publishing research! Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization. - Develop and test new signals for improving recommendation models - Use supervised and uplift learning algorithms to improve customer experience - Contribute to production code and science tooling - Design A/B tests and conduct statistical analysis on their results - Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers - Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area - Present and publish science research internally and externally, contributing to Amazon's science community - Mentor junior engineers and scientists. About the team Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team. We also highly value having a big impact, both for Amazon's business and for our customers. Basic Qualifications: - 3+ years of building models for business application experience - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience - Experience in patents or publications at t
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