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Senior Data Scientist / Machine Learning Engineer

Conexess

Senior Data Scientist / Machine Learning Engineer

Conexess is a full-service staffing firm offering contract, contract-to-hire, and direct placements. We have a wide range of recruiting capabilities, from help desk technicians to CIOs. We are also capable of offering project-based work.

Our client is looking for a Senior Data Scientist / Machine Learning Engineer to join their rapidly growing team that has deep optical transport-network domain experience. This role applies advanced analytics and ML to legacy and modern telecom transport networks to proactively identify outages, capacity risk, and migration issues. We are looking for someone who has worked directly with real transport data in multi-vendor environments.

  • Apply data science and ML to SONET/TDM and DWDM/OTN transport networks
  • Ingest, normalize, and analyze large-scale network inventory, topology, performance, and alarm data
  • Build models and analytics to:
    • Predict network outages
    • Identify capacity exhaustion and congestion risk
    • Support legacy network consolidation and migration planning
    • Translate internal and customer requirements into production-ready analytics
    • Work directly with software engineering and product teams to deploy solutions
    • Engage with customers as a technical expert when needed

Preferred Skills

  • 5+ years in telecom, transport networking, or network analytics
  • Strong background with graph databases (e.g., Neo4j or equivalent)
  • Strong hands-on knowledge of:
    • Legacy transport: SONET, SDH, TDM hierarchies
    • Modern transport: DWDM, OTN, optical channel modeling
    • Proven experience organizing and analyzing complex, multi-vendor network data
    • Strong Python and SQL skills
    • Experience delivering production analytics or ML solutions
  • Ability to operate across engineering, product, and customer teams
  • Background with OSS, network inventory systems, or transport planning tools
Vacancy posted 4 days ago
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