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# Senior Data Scientist, Alexa For Shopping (Rufus)

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $159,200.0 - $215,300.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Alexa, Amazon Web Services, Data Analysis, Communications Protocols, Databases, Query Languages, Dimensional Modeling, R (Programming Language), Graph Database, Data Intelligence, Python (Programming Language), Logistic Regression, MATLAB, Machine Learning, Mathematical Software, Neo4j, Named Entity Recognition, NumPy, SAS (Software), SciPy, Search Technologies, SQL Databases, System Testing, Unstructured Data, Data Processing, Scripting, Multi-Agent Systems, Generative AI, Pandas, Machine Learning Operations
- **Published:** July 23, 2026
- **Apply:** https://www.careerjet.com/job/us7474b470a5d08a57f13c1d1887047522/eaa

## About the Role

4+ years of data scientist experience

 - 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
 - Experience with statistical models e.g. multinomial logistic regression
 - 5+ years of working with Data & AI related technologies, including, but not limited to, AI/ML (Artificial Intelligence/Machine Learning), GenAI (Generative AI), Analytics, Database, and/or Storage experience
 - Python proficiency - statistical modeling, data manipulation (pandas, numpy, scipy), and scripting across ML pipelines and evaluation infrastructure
 - Demonstrated experience extracting structured signal from unstructured text at scale - NLP pipelines, intent classification, entity extraction, or equivalent
Preferred Qualifications

 - Experience with multi-agent system evaluation and independently and end-to-end
 - Production RAG or retrieval system experience - embedding strategy, vector search, hybrid retrieval, similarity threshold calibration
 - AWS Bedrock or Strands SDK experience - or equivalent orchestration framework (LangGraph, CrewAI, AutoGen)
 - Graph database experience (Neptune, Neo4j) - schema design, traversal queries, knowledge graph construction
 - Experience scaling NLP inference pipelines - model sizing decisions, batching strategy, SageMaker or equivalent endpoint optimization
 - Business intelligence or analytics domain background - metric definitions, dimensional modeling, causal inference
 - Track record of publishing at peer-reviewed venues or presenting at industry conferences

## Description

We are building a agentic intelligence system that transforms unstructured, noisy customer-data into actionable intelligence for product analytics to guide evolution of Amazon Shopping CX's - surfacing metrics on demand and insights unprompted, without an analyst in the loop.
We are solving one of the hardest problems in the agent driven data intelligence space to isolate insights from noise. This role will own multi agent system orchestration and context management; self-improving agent layer that gets measurably better over time without human intervention and reliable signal extraction from unstructured data and proactive intelligence that detects what matters before anyone asks.
Our agentic system is in production. What we don't yet have is a system that evaluates its own output quality, identifies where it fails, and closes that feedback loop automatically., As Senior Data Scientist, you will own the multi agent orchestration and the self-improvement system end-to-end. You will also own designing the overall architecture to extract insights from unstructured data at scale. You will work directly with the principal engineer, influence the technical roadmap across the team, and partner with SDE's.

 - This role requires operating independently on problems that are not well-defined or structured, identifying and framing research challenges across broad problem areas, and delivering end-to-end solutions that have significant impact on the product.
 - Own the multi-agent topology (Planner * Worker * Reasoner * Loop Controller) - inter-agent communication protocols, and loop termination logic
 - Design and manage the context window strategy across agents
 - Own all system prompts, routing prompts, and chain-of-thought scaffolding across agents
 - Define what "better" means across dimensions (factual grounding, hypothesis novelty, evidence completeness, reasoning coherence) without ground-truth labels at scale
 - Design how eval signal propagates back into prompt updates and model routing decisions
 - Own schema grounding, sparse vector indexing, and domain-scoped kNN queries
 - Own embedding strategy, intent classification accuracy, and entity extraction quality, Overview: Lynker Corporation is seeking a Senior Fisheries Data Scientist - Salmon Survival, Migration, and Mark-Recapture Modeling to support NOAA Fisheries' Northwest Fisheries…
 + 18 days ago


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