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.
As a Senior Data Scientist (DS), you will drive the development and implementation of advanced analytics and machine learning solutions. You will work on critical initiatives including natural language processing for non-conformance analysis, statistical process controls (SPC) for test optimization, and equipment predictive maintenance models to enable manufacturing rate acceleration. Your work will directly influence Kuiper’s production manufacturing workflow.
You are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers, and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.
Key job responsibilities
Lead the design and implementation of ML/LLM solutions to analyze manufacturing data and identify failure patterns and operational risks
Design predictive models for statistical process control and equipment maintenance optimization
Build production-ready ML pipelines leveraging AWS services (e.g., SageMaker, Bedrock, AWS Glue)
Formalize assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them
Develop and test model enhancements, running computational experiments, and fine-tuning model parameters for new models
Collaborate with engineering teams to translate complex manufacturing challenges into data-driven solutions
Drive consensus on metrics and analysis approaches to support business strategy
Write documents and create compelling visualizations and presentations to communicate insights to stakeholders
Mentor team members and drive data science best practices across the organization
About the team
Established in 2023 as ProdOps was preparing to transition from development into full-rate production, OCC was purpose-built to ensure that as manufacturing scaled, the most critical problems would be identified, solved, and prevented from recurring through a single integrated operating system. OCC focuses on the most critical problems for ProdOps at each phase of production. The team operates as a closed-loop flywheel where each rotation identifies value, solves problems, implements change, and generates new data that feeds the next cycle. Every rotation compounds returns.
