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Medium: Tier-1 brand and mid-level ML role, but niche network-telemetry specialization limits candidate pool.
High: role requires specialized network telemetry and optical/hardware diagnostics knowledge, reducing cross-industry transferability.
High: explicit 5+ years plus mandatory advanced ML, Spark, and production MLOps skills and domain knowledge.
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Design, deploy, and optimize data pipelines, statistical algorithms, and ML models for predictive assurance and real-time health analytics of enterprise-grade network routing hardware.
Develop time-series forecasting models and statistical health index algorithms to detect and predict hardware and optical system failures.
Lead production transition of ML models ensuring scalability, low-latency, and high availability, collaborating with network engineers and architects for integration into monitoring workflows.
5+ years of professional experience in Data Science, Machine Learning, or AI Engineering roles.
Strong proficiency in Python and deep learning frameworks like PyTorch or TensorFlow.
Experience with Apache Spark (PySpark/Scala) in production environments for large-scale data processing.
PhD in Statistics, Operations Research, Computer Science, or equivalent with 3+ relevant years, or Master's degree with 5-6+ years of relevant experience.
Expertise in time-series forecasting, anomaly detection, and advanced multivariate statistical analysis focused on network telemetry data.
Proven ability to engineer production-grade machine learning pipelines including containerization (Docker, Kubernetes) and MLOps practices.
Experience working with infrastructure/network telemetry, optical systems diagnostics, and integrating ML insights into enterprise network hardware monitoring.