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Data Scientist, Automated Vehicle Testing

Amazon

Data Scientist, Automated Vehicle Testing

full-timePosted: Aug 19, 2026Updated: Aug 27, 2026Redmond, Washington, United States

Job Description

Amazon Leo is Amazon’s low Earth orbit satellite broadband network. Its mission is to deliver fast, reliable internet to customers and communities around the world, and we’ve designed the system with the capacity, flexibility, and performance to serve a wide range of customers, from individual households to schools, hospitals, businesses, government agencies, and other organizations operating in locations without reliable connectivity. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. We are looking for an experienced Data Scientist to help architect state-of-the-art test infrastructure and lead the development of data models and analysis tools to represent the ground truth about satellite test results in order to facilitate important business decisions. Our team is responsible for core infrastructure and tools that will serve as the backbone of automated satellite testing operations to enable rapid scaling of manufacturing processes. Key job responsibilities * Work with engineering, software and manufacturing teams to understand drivers, impacts, and key influences on satellite performance * Lead the design, build and implementation of production models and make decisions in real time for satellite test results * Drive actions at scale to optimize test methodology and drive increases to satellite reliability * Analysis and modeling of satellite telemetry from test results in lab and on-orbit * Develop models and data pipelines for satellite telemetry * Create and manage datasets for continued pre-training and supervised fine-tuning of LLMs * Develop scalable visualizations for analysis of satellite performance A day in the life As Amazon Leo Data Scientist you will own the architecture definition and development of data analysis tools to to aid engineering and production teams in deciding flight-worthiness of each Amazon Leo satellite and historical traceability tools to enable simplified discovery and interpretation of past test data. You will work with multiple engineering, software and manufacturing teams across ground and space systems, to specify requirements, define data collection, interpretation strategies, data pipelines and implement data analysis and reporting tools for Integrated Vehicle tests. Your focus will be in optimizing the analysis of test results to enable Amazon Leo production plans. About the team The Automated Vehicle Testing Team is a mix of scientists and software engineers responsible for data infrastructure, tools, and research that serve as the backbone of automated satellite testing operations to enable rapid scaling of manufacturing processes.

Locations

  • Redmond, Washington, United States
  • Redmond, Washington, United States

Salary

136,000 - 184,000 USD / yearly

Skills Required

  • machine learning conceptsintermediate
  • Pythonintermediate
  • definingintermediate

Required Qualifications

  • 2+ years of data scientist experience (experience, 2 years)
  • 3+ 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, 3 years)
  • 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience (experience, 3 years)
  • 1+ years of working with or evaluating AI systems experience (experience, 1 years)
  • Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM) (experience)
  • Experience applying theoretical models in an applied environment (experience)

Preferred Qualifications

  • Knowledge of machine learning concepts and their application to reasoning and problem-solving (experience)
  • Experience in Python, Perl, or another scripting language (experience)
  • Experience in a ML or data scientist role with a large technology company (experience)
  • Experience in defining and creating benchmarks for assessing GenAI model performance (experience)
  • Experience working on multi-team, cross-disciplinary projects (experience)
  • Experience applying quantitative analysis to solve business problems and making data-driven business decisions (experience)
  • Experience effectively communicating complex concepts through written and verbal communication (experience)

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