Data Scientist

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: instrumentation requirements, pipeline design, validation, versioning, and data quality monitoring Develop performance analytics for overall end-to-end Satellite–RAN performance projected over 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 measured KPIs/KQIs, including confidence/uncertainty measures Develop fleet service scheduling models, linking orbital state/visibility, predicted SAT-RAN capacity and quality, interference, and spacecraft power/thermal constraints to achievable service performance and demand fulfillment. Build decision-support data products: dashboards, health scores, early-warning indicators, incident enrichment, and executive-ready reporting for SAT-RAN performance Partner with engineering teams to define success metrics, run backtesting/regression evaluation, and operationalize models into workflows (assurance, release validation, optimization loops) Establish best practices for reproducibility and MLOps: model monitoring, drift detection, dataset/version governance, and ation 5+ years of experience delivering data science / ML solutions in production (monitoring, anomaly detection, forecasting, quality scoring, optimization support) Strong proficiency in Python (pandas/NumPy/scikit-learn and time-series tooling) and strong SQL skills Demonstrated experience defining and operating data models and analytics pipelines (schemas, identifiers, aggregation logic, data validation, lineage) Strong statistical foundations (model evaluation, uncertainty, time-series behavior, bias/variance, backtesting) Ability to translate cross-domain system problems into measurable metrics and deployable analytics Bachelor’s or Master’s degree in CS/EE/Statistics/Math/Physics or related technical field (or equivalent experience) Preferred Qualifications Multivariate anomaly detection at scale (change-point detection, sequence models where justified, graph/topology-aware features) Telecom/systems experience: LTE/5G KPIs/KQIs, OSS counters/alarms, QoE/QoS metrics, RAN performance indicators Familiarity with scheduling/capacity modeling concepts (resource all