Are you passionate about building the world's largest low-power, long-range IoT network? Do you want to use data science to expand connectivity for billions of devices and directly shape how Amazon Sidewalk grows its coverage footprint?
If turning IoT telemetry into actionable insights excites you, we want to talk.
The Amazon Sidewalk team is looking for an analytical and technically skilled Data Scientist to join our team. In this role, you will design, build, and deploy machine learning models and analytical solutions that power Sidewalk's coverage map, optimize network performance, and drive insights from one of the largest mesh IoT networks in the world.
Amazon Sidewalk is a shared network that helps devices like Ring, Echo, and third-party IoT endpoints work better at home and beyond the front door. Sidewalk uses Bluetooth Low Energy (BLE), 900 MHz LoRa, and FSK radio protocols to extend the working range of low-bandwidth devices through a network of millions of Sidewalk-enabled gateways. This role puts you at the center of understanding and expanding that network.
This role offers wide scope, autonomy, and ownership. You will work closely with software engineers, data engineers, and product managers to put algorithms into practice. You should have strong business judgment, excellent written and verbal communication skills, and be willing to take on challenging initiatives both independently and as part of a team.
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.
Basic Qualifications
- 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
Preferred Qualifications
- 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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