Amazon.com Services LLC
Machine Learning Science, Applied Science, consumer engagement
AppliedScientist,CustomerBehaviorAnalytics
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Applied Scientist, Customer Behavior Analytics at Amazon.com Services LLC. Skills: Machine learning, Customer behavior analytics, Generative models, Deep learning. Design and fine-tune language and generative models. Develop generative recommendation and decision models”
What You'll Achieve.
Enhance customer experiences; Strengthen customer relationship; Drive personalized experiences; Make significant business impact; Drive measurable actions; Optimize for long-term customer value; Enable long-horizon reasoning; Support efficient, incremental inference; Align model behavior with long-term engagement; Align model behavior with satisfaction; Align model behavior with retention metrics; Measure immediate impact; Measure long-term customer outcomes; Focus on measurable customer satisfaction; Focus on business impact; Improve customer satisfaction; Improve customer loyalty
Industry & Context.
Analytical problem solver; Exploring data; Problem-solving efforts; Algorithm development
What They're Looking For.
Must Have
3+ years building models, PhD or Master's degree, 4+ years CS, CE, ML experience, Experience programming Java, C++, Python, Experience algorithms and data structures, Experience parsing, Experience numerical optimization, Experience data mining, Experience parallel and distributed computing, Experience high-performance computing, 4+ years practical machine learning experience, Experience written and verbal communication
Nice to Have
PhD preferred, Experience building ML models, Experience developing algorithms, Deep learning models architecture design, Deep learning training and optimization, Model pruning
What You'll Do.
Design and fine-tune language and generative models
Develop generative recommendation and decision models
temporal representations of customer behavior
Integrate representations into generative and deep learning models
Experiment scalable representations of customer and household behavior
Summarize long engagement history into compact states
Design and apply post-training optimization techniques
Align model behavior with long-term metrics
Develop robust evaluation frameworks
How You'll Work.
Team & Collaboration
Collaborate with scientists and engineers; Collaborate with business, marketing, and engineering teams; Collaborate with technical teams; Collaborate with business stakeholders
Communication Scope
Present complex technical information
Full Job Description
The Customer Behavior Analytics team designs innovative machine learning solutions to enhance customer experiences and strengthen their relationship with Amazon. This interdisciplinary team of scientists and engineers incubates and develops disruptive solutions using state-of-the-art technology to tackle some of the most challenging scientific problems in customer behavior analysis at Amazon. To achieve this, the team utilizes methods from deep learning, large language models (LLMs), natural language models, recommendation systems, affinity models, reinforcement learning, and econometrics to drive personalized experiences throughout the customer journey. As a Customer Behavior Analytics Scientist, you will have the opportunity to make a significant business impact, delve into large-scale problems, drive measurable actions, and collaborate closely with other scientists and engineers. You will be responsible for designing and developing state-of-the-art models and working with business, marketing, and engineering teams to address key challenges in customer behavior analytics. Key responsibilities include: - Design and fine-tune language and generative models for recommendation and engagement, including continued pre-training, supervised fine-tuning, and preference-based alignment, to optimize for long-term customer value rather than short-term clicks. - Develop generative recommendation and decision models that produce next-best customer engagement actions (e.g., recommendations, bundles, messaging, incentives, timing), conditioned on structured customer and household-level behavioral context. - Build structured, temporal representations of customer behavior (e.g., lifecycle stage, needs, replenishment patterns, engagement history) and integrate them into generative and deep learning models to enable long-horizon reasoning. - Experiment scalable representations of customer and household behavior that summarize long engagement history into compact states, supporting ef
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