Key job responsibilities
Build and improve Sidewalk's coverage forecast models that predict network availability across BLE, LoRa, and FSK link types for any geographic location.
Analyze gateway telemetry, device event data, and registration statistics from the Sidewalk Data Lakehouse to identify coverage gaps, network health trends, and device behavior patterns.
Develop geospatial models that leverage gateway location data, signal propagation characteristics, and device density to optimize coverage map accuracy.
Design anomaly detection systems to identify gateway outages, data limit issues, key refresh failures, and device provisioning problems at scale.
Research and develop Generative AI and LLM-based solutions for automated device troubleshooting and root cause analysis across the Sidewalk network.
Define and track key network health metrics including coverage scores, gateway online rates, device registration success rates, and data usage patterns.
Drive end-to-end statistical analysis on complex, ambiguous problems such as predicting optimal gateway placement, modeling RF propagation in urban vs. suburban environments, and forecasting network growth.
Partner with engineering teams to productionize models into Sidewalk's cloud services, ensuring coverage predictions and device analytics are available in real time.
About the team
The Amazon Sidewalk team builds and operates a crowd-sourced IoT network, connecting millions of gateways (Ring, Echo, and third-party devices) to provide low-bandwidth connectivity for IoT endpoints. Our platform handles device provisioning,
authentication, data routing across BLE/LoRa/FSK protocols, coverage forecasting, and gateway management. The network supports use cases ranging from smart home device connectivity to asset tracking and pet location services. Our data infrastructure processes hundreds of millions of device events daily serving insights to engineering, product, and partner teams.
2+ years of data scientist experience
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
3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
1+ years of guiding and coaching a group of researchers experience
1+ years of working with or evaluating AI systems experience
Experience applying theoretical models in an applied environment
Knowledge of machine learning concepts and their application to reasoning and problem-solving
Experience working on multi-team, cross-disciplinary projects
Experience applying quantitative analysis to solve business problems and making data-driven business decisions
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