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Remote, popular data/ML role but senior level and niche telemetry/graph requirements moderate applicant competition.
Strong domain bias for networking, telemetry, and graph-based production ML makes cross-industry transfer limited.
Requires specialized production ML, Kafka, graph DB, and telemetry expertise, enforcing strict technical filters.
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Build, deploy, and maintain machine learning models for entity resolution, deduplication, and confidence scoring directly in production streaming pipelines involving network telemetry data.
Own the full lifecycle of these models including monitoring, retraining, and incident response to maintain model performance and accuracy in live customer infrastructure data.
Collaborate across teams (security, alerting, observability) to apply data science methods beyond classification, including anomaly detection and telemetry pattern recognition.
Experience working hands-on with production machine learning model deployment and operations (monitoring, retraining, incident response).
Proficiency in production-level Python coding with tested, maintainable code within a shared engineering codebase.
Hands-on experience with Kafka or similar event-driven streaming systems, and graph databases such as Memgraph or Neo4j.
Working knowledge of networking fundamentals and telemetry protocols (SNMP, syslog, OpenTelemetry) with experience handling large-scale telemetry or observability data.
Demonstrated ability to own end-to-end model lifecycle in production environments involving complex, noisy infrastructure telemetry data.
Experience translating raw telemetry and network protocol data into effective machine learning model features, showing deep domain understanding.
Comfortable working in a technically complex, cross-functional environment with exposure to streaming data pipelines, graph databases, and infrastructure telemetry analytics.