Senior Data Scientist – Payload & SAT-RAN

About the Role

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.

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 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 such as assurance, release validation, and optimization loops.
• Establish best practices for reproducibility and MLOps, including model monitoring, drift detection, dataset/version governance, and documentation.

Qualifications

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Statistics, Mathematics, Physics, or a related technical field, or equivalent experience.
• 5+ years of experience delivering data science/ML solutions in production, including monitoring, anomaly detection, forecasting, quality scoring, or 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, including schemas, identifiers, aggregation logic, data validation, and lineage.
• Strong statistical foundations, including model evaluation, uncertainty, time-series behavior, bias/variance, and backtesting.
• Ability to translate cross-domain system problems into measurable metrics and deployable analytics.

Preferred Qualifications

• Experience with multivariate anomaly detection at scale, including change-point detection, sequence models where justified, and graph/topology-aware features.
• Telecom/systems experience, including LTE/5G KPIs/KQIs, OSS counters/alarms, QoE/QoS metrics, and RAN performance indicators.
• Familiarity with scheduling/capacity modeling concepts, including resource allocation, interference-aware capacity, constraint modeling, and power/thermal-limited regimes.
• MLOps experience, including deployment, monitoring, drift detection, and CI/CD for data and models.

Skills

• Python, including pandas, NumPy, scikit-learn, and time-series analysis libraries.
• SQL and relational/analytics data stores.
• Multivariate time-series and anomaly/change-point detection frameworks.
• Telemetry, OSS counters/alarms, and KPI/KQI data pipelines.
• MLOps tooling for model monitoring, drift detection, versioning, and CI/CD.
• Dashboarding and reporting platforms for health scores and executive-facing analytics.

Physical Requirements

• Ability to lift up to 25 lbs.
• Ability to use a computer for extended periods.
• Ability to work in a standard office environment.
• Ability to travel occasionally to support cross-team collaboration or reviews with engineering and operations teams as needed.