📋 Description

• Design, build, and improve machine learning models and graph/statistical algorithms — including
• Build a real, repeatable experimentation and model-deployment workflow (e.g., using MLflow or
• Work directly with the near-real-time engineering team to identify where today's rule-based
• Mine large-scale time-series and topology data (stored in Elasticsearch, InfluxDB, and MongoDB) to
• Define and track quantitative success metrics so every model shipped can be proven to actually
• Present findings and roadmap recommendations to engineering and business leadership.

🎯 Requirements

• 5+ years of applied data science / machine learning experience, including graph algorithms
• Strong Python skills; comfort working alongside Go-based production services.
• Experience with large-scale time-series and document data stores (Elasticsearch, InfluxDB, MongoDB
• Experience with ML experiment tracking and deployment tooling (MLflow or equivalent) and with
• Excellent communication skills — the ability to turn open-ended 'why is the network behaving this
• Outstanding B.Sc. graduates in these fields may also be considered.

🎁 Benefits

• Background in telecom, RF, or wireless networking (handovers, KPIs such as RSRP or PRB utilization
• Experience turning a hand-tuned, rule-based system into a learned model running in a live
• Experience with distributed or streaming compute frameworks.