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Senior Data Scientist
Not specifiedNot specifiedData Science
Develop and operationalize production data science solutions for satellite and RAN systems, including performance analytics, anomaly detection, quality scoring, forecasting, and service scheduling.
Responsibilities
- Define data strategy, subsystem metrics, data sources, and collection requirements across payload, gateway, transport, core, and RAN layers.
- Design and maintain data models that unify satellite-RAN telemetry across domains.
- Lead production deployment of data models, instrumentation, pipelines, validation, versioning, and data-quality monitoring.
- Develop end-to-end satellite-RAN performance analytics and attribute impacts across system layers.
- Build anomaly detection and prevention systems using multivariate time series, correlation and causality approaches, topology-aware features, and alert triage scoring.
- Develop quality scoring models combining predictive signals, measured KPIs and KQIs, and confidence measures.
- Create fleet service scheduling models incorporating orbital visibility, capacity, quality, interference, spacecraft power and thermal constraints, and demand fulfillment.
- Build dashboards, health scores, early-warning indicators, incident enrichment, and executive reporting.
- Partner with engineering teams on success metrics, backtesting, regression evaluation, and operationalizing models in assurance, release validation, and optimization workflows.
- Establish reproducibility and MLOps practices, including model monitoring, drift detection, and dataset and version governance.
Requirements
- At least 5 years of experience delivering production data science or machine learning solutions.
- Strong Python skills, including pandas, NumPy, scikit-learn, and time-series tools.
- Strong SQL skills.
- Experience defining and operating data models and analytics pipelines, including schemas, identifiers, aggregation logic, validation, and lineage.
- Strong statistical foundations in model evaluation, uncertainty, time-series behavior, bias and variance, and backtesting.
- Ability to translate cross-domain system problems into measurable metrics and deployable analytics.
- Bachelor’s or master’s degree in computer science, electrical engineering, statistics, mathematics, physics, or a related technical field, or equivalent experience.
Nice to have
- Experience with multivariate anomaly detection at scale, including change-point detection, sequence models, and topology-aware features.
- Telecommunications or systems experience with LTE/5G KPIs and KQIs, OSS counters and alarms, QoE/QoS metrics, or RAN performance indicators.
- Familiarity with scheduling and capacity modeling.