Data Science Engineer (Infrastructure & Network Analytics)
Core
Design, deploy, and optimize data pipelines, statistical algorithms, and machine learning models to monitor, analyze, and forecast the health of enterprise-grade routing fleets and optical layers.
Role type
Senior IC Data Science Engineer (Infrastructure & Network Analytics)
Builds
Predictive Assurance and Real-Time Health Analytics platform for HPE's enterprise routing fleet (Juniper QFX Series nodes)
Domain
Telecom infrastructure, optical networks, and network telemetry
Deliverable
production ML models
Required skills
Python, PyTorch/TensorFlow, Apache Spark (PySpark/Scala), time-series forecasting, anomaly detection, multivariate analysis, CI/CD, Docker, Kubernetes, SQL, Kafka
Preferred skills
LightGBM/XGBoost, Deep Learning (LSTMs/BiLSTMs), network monitoring systems, optical transceiver diagnostics, statistical profiling (Z-score, Change-Point Detection), MLOps tools (MLflow, Kubeflow)
Technologies
PySpark, TensorFlow, PyTorch, LightGBM, XGBoost, Kafka, Docker, Kubernetes, MLflow, Kubeflow
Responsibilities
Design and refine time-series forecasting models for optical performance and failure markers; translate complex network telemetry into actionable features for real-time anomaly detection; lead the transition of models from R&D to production Datacenter Assurance platform; develop statistical 'Health Index' algorithms to identify degraded optics; partner with network hardware engineers and software architects to integrate data-driven insights.
Seniority
Senior, hands-on IC
