Data Science Engineer (Infrastructure & Network Analytics)

Job Summary

Data Science Engineer (Infrastructure Network Analytics).

This role has been designed as Hybrid with a requirement that you will work on average 2 days per week from an HPE office.

What You'll Do

We are seeking a highly skilled Data Science Engineer to drive the development of our next-generation Predictive Assurance and Real-Time Health Analytics platform. In this role, you will design, deploy, and optimize data pipelines, statistical algorithms and machine learning models that monitor, analyze, and forecast the health of our enterprise-grade routing fleet (including Juniper QFX Series nodes).

You will bridge the gap between heavy-duty Data Engineering and Advanced Machine Learning, implementing stateful batch analytics engines to detect insidious regressions like memory leaks, alongside deep learning models to predict physical hardware failures in our optical layer.

• Predictive Modeling: Design and refine time-series forecasting models (e.g., BiLSTM, Transformers, or Prophet) to predict optical performance and failure markers.
• Feature Engineering: Translate complex network telemetry (DOM metrics, FEC counters, BER, and thermal data) into actionable features for real-time anomaly detection.
• Distributed Computing Data Pipelines: 5+ years of production experience with Apache Spark (PySpark/Scala) utilizing advanced windowing, state manipulation, and memory-efficient aggregations.
• Machine Learning Frameworks: Proven experience deploying LightGBM (or XGBoost) and Deep Learning frameworks (TensorFlow/Keras or PyTorch for LSTMs/BiLSTMs ) into live production environments.
• Production Engineering: Lead the transition of models from RD/Lab environments into our production Datacenter Assurance platform, ensuring scalability, low-latency, and high availability.
• Diagnostic Analytics: Develop statistical "Health Index" algorithms to identify currently degraded optics, moving beyond simple threshold alerts to intelligent, multivariate diagnostics.
• Collaboration: Partner with network hardware engineers and software architects to understand failure signatures and integrate data-driven insights into our monitoring workflows.

What You Need to Bring

• Experience: 5+ years of professional experience in a Data Science, Machine Learning, or AI Engineering role.
• Core Skills: Expert-level proficiency in Python and deep learning frameworks (PyTorch or TensorFlow).
• Modeling: Strong background in time-series forecasting, anomaly detection, and multivariate analysis.
• Engineering: Production-level coding experience; familiarity with CI/CD, Docker, Kubernetes, and MLOps best practices.
• Data Handling: Proficiency with SQL and large-scale data processing frameworks (e.g., Apache Spark, Kafka); bonus skills - Apache Flink, Apache Storm.
• Domain Knowledge: Familiarity with network telemetry, signal processing, or hardware performance metrics is a significant plus.

Preferred Skills

• Experience with network monitoring systems or optical transceiver.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.