Data Scientist, Phased Array Antenna System Validation

Amazon LEO is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Amazon Leo will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies, and other organizations operating in places without reliable connectivity. About this role We're looking for a Data Scientist to turn data from our satellite fleet into engineering decisions. Every satellite in orbit sends back a steady stream of telemetry. Every unit was also tested heavily on the ground before launch, and the ground segment watches the satellite every day while it operates. Your job is to connect these sources. You'll find out which in-orbit behaviors trace back to what we measured in the factory, which events repeat across the fleet, and which patterns point to weaknesses in the hardware design. Your findings will directly shape how we design, build, test, and operate the next generation of satellites. A typical problem starts small: a handful of satellites show a drifting temperature, an occasional link dropout, or a counter that creeps up faster than expected. You'll work out whether it's a real effect or noise, whether it's spreading, and what the affected units have in common. They might share a manufacturing lot, a configuration, an orbital environment, or a signature in their factory test history. From there you'll form a hypothesis about the physical cause and argue it in front of hardware, firmware, and reliability engineers who will push back hard. You'll also have to say plainly when the data rules your own idea out. Telemetry alone rarely settles the question, so your first stop is the ground test data we already have. Every unit in orbit has a detailed record from manufacturing and environmental testing, and the fleet as a whole is backed by an even larger population of units still on the ground. You'll dig through that history for the same behavior: did the affected units show early signs on the ground, did they sit near a test limit, does the effect appear across many units under certain conditions? Often the answer is already in data no one has looked at in this light. When it isn't, you'll design the ground experiments that can provide it. That means deciding which units to test and under what conditions, how many runs you need before the result means anything, and what outcome would confirm or refute the theory. You'll work with the engineers who run those tests, analyze the results, and close the question with a clear answer and a recommended action. Beyond individual investigations, you'll build the fleet-level view: baselines for healthy behavior, early warning indicators that catch emerging issues before they cause outages, and reliability estimates that tell us how the population is aging. You'll decide which recurring analyses should become automated monitoring and work with software engineers to make that happen. You'll be the person the team turns to when someone asks "is this normal?" about the fleet. The ideal candidate is curious about how hardware fails and comfortable with messy data from many sources. They can turn "we're seeing something odd on a few satellites" into a rigorous analysis and a clear recommendation. What you'll bring: - Master's degree or above in a quantitative field such as statistics, mathematics, data science, physics, electrical or aerospace engineering, or computer science, or PhD - 3/5+ years of hands-on experience in an applied or industry setting using Python and SQL to analyze large datasets such as time series, telemetry, sensor, or test data - Experience applying statistical methods (hypothesis testing, regression, time-series analysis, anomaly detection) to noisy, incomplete real-world data - Experience building visualizations and dashboards that communicate findings to engineers and decision makers - Experience framing and solving loosely defined problems, from the first question to a recommendation people act on - Ability to work with hardware and software engineers and turn their questions into analyses you can actually run Nice to have: - Experience analyzing telemetry from satellites, spacecraft, aircraft, vehicles, or other fleets of deployed hardware - Experience with reliability and failure analysis methods such as survival analysis, Weibull modeling, failure-mode analysis, and root-cause investigation - Familiarity with electronics or RF hardware, manufacturing test, environmental stress screening (thermal cycling, burn-in, HASS/HALT), or yield analysis - Experience with pattern-discovery techniques such as clustering, change-point detection, and unsupervised anomaly detection - Experience with large-scale data tools (Spark, Athena/Presto, Redshift) and AWS - Experience with visualization tools such as QuickSight, Tableau, Grafana, or Plotly/Dash - Experience working with software engineers on data pipelines: data requirements, schema design, data quality checks, and automating recurring analyses - Experience using AI-assisted tools to speed up analysis and automate repetitive work - A track record of changing engineering design or test decisions with data 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. Key job responsibilities As a Data Scientist, you will… - Analyze in-orbit telemetry to measure the health and performance of satellite subsystems, and detect anomalies and slow degradation early - Correlate in-orbit behavior with pre-launch data, including manufacturing, production test, and environmental test results, to find which ground measurements predict on-orbit performan