astspacemobile · Lanham, Maryland, United States
Skills: Python, SQL, CI/CD
AST SpaceMobile and our global partners are building the first and only space-based cellular broadband network to be accessible by standard smartphones. Called SpaceMobile, this ultra-powerful network is being designed to provide connectivity at 4G/5G speeds everywhere on the planet – on land, at sea and in flight. Position Overview The Senior Data Scientist – Payload & SAT-RAN defines, builds, and operationalizes SAT-RAN data models across Payload, Gateway (GW), and Cellular/RAN subsystems. This role focuses on creating robust telemetry-driven models and metrics that quantify system performance, detect and prevent failures and anomalies, and enable data-driven optimization of user experience, capacity, and duty cycle under various ground and in-orbit constraints. This person works closely with payload engineering, gateway/network engineering, RAN/system architects, and operations to translate complex, multi-domain data into actionable insights and production-grade analytics. Key Responsibilities: Own Payload/GW/SAT-RAN data strategy: define key subsystem metrics, data sources, and collection requirements spanning payload, gateway, transport, and RAN/service layers. Design and maintain SAT-RAN subsystem data models (entities, relationships, identifiers, etc.) to unify telemetry across domains and support scalable analytics. Lead data model definition and deployment into production systems, including instrumentation requirements, pipeline design, validation, versioning, and data quality monitoring. Develop performance analytics for end-to-end Satellite–RAN performance projected across multiple subsystems, including attribution of impact across payload, GW, transport, core, and RAN layers. Build failure/anomaly detection and prevention systems using multivariate time-series, correlation/causality-informed approaches, topology/context-aware features, and alert deduplication/triage scoring. Create qualitative quality scoring models that combine predictive signals with m…
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