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 performance. Feed those insights back into test limits and screening strategy
Correlate in-orbit data with what the ground segment sees (gateways, customer terminals) and with failure analysis of lab and returned units
Connect events across the fleet by build lot, component, configuration, firmware version, orbit, and operating conditions, so we can tell systemic issues apart from one-off events
Find patterns that point to hardware design weaknesses, measure their impact on the fleet, and present the evidence to hardware engineering teams with clear recommendations
Build visualizations and dashboards that support decisions in fleet health reviews, anomaly investigations, and design reviews
Work with software engineers to improve the data pipeline: define data requirements, improve data quality, link ground and in-orbit datasets, and automate recurring analyses
Own ambiguous investigations end to end, from forming the hypothesis to reporting the outcome to senior engineering leaders
About the team
Phased Array Antenna System Validation is the team that decides whether a satellite's phased array antennas (PAAs) are ready to fly. These are the electronically steered transmit and receive panels that form the link between the satellite and the ground. We're part of Amazon Leo, Amazon's low Earth orbit satellite constellation, and we own the test strategy, the automation, and the data pipeline that every PAA goes through on its way to orbit. That path runs from early component-level screening through full-panel functional testing and extended environmental stress testing across temperature, vacuum, and vibration. Our responsibility doesn't end at launch. We also own the analysis of telemetry from PAAs in orbit, and we use what the constellation tells us to improve how we test on the ground.
We are a hardware-facing software and test team, and we sit where several disciplines meet. Our test automation directs firmware provisioning, self-tests, and environmental profiles on test stations at manufacturing sites. Over a single run it exercises thousands of measurement points: high-speed serial links, clocking, digital beamformer firmware, RF chains, power, and thermal behavior. Every result is tied to a specific serial-numbered part. That builds a detailed, part-level history of how each panel and component behaved under stress, from the factory floor to launch.
A large part of our work is investigation. When something fails, we work out whether the cause is the hardware, the firmware, the test equipment, or our own automation, and we decide what should happen next. Over time we have built a growing catalog of failure signatures and their root causes, internal tools for analyzing long-duration tests, and AI-assisted agents that help turn raw logs into conclusions. We care about evidence over assumption. A label on a result isn't the result itself, and a search that finds nothing doesn't prove the problem has no history. Theories get tested against the data before they're accepted.
We support multiple hardware generations at once, so the architecture, the questions, and the tooling keep changing. You'll work closely with antenna hardware, firmware, manufacturing, and reliability engineers who are glad to dig into a hard problem with you. Because we own both ends, from the first ground test to behavior in orbit, the loop is closed within the team. What we learn from the constellation goes straight back into our screens, thresholds, and test sequences, so that the next panel we validate is more likely to keep working in orbit.
2+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
2+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
Experience diving into data to discover hidden patterns and of conducting error/deviation analysis
Experience developing experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
Experience applying theoretical models in an applied environment
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Redmond - 136,000.00 - 184,000.00 USD annually
