Senior Data Scientist — Network Intelligence

What You'll Do:

• Design, build, and improve machine learning models and graph/statistical algorithms — including clustering, anomaly detection, and time-series modeling and forecasting — to drive automated network optimization.. 

• Build a real, repeatable experimentation and model-deployment workflow (e.g., using MLflow or comparable tooling), taking models from notebook to production. 

• Work directly with the near-real-time engineering team to identify where today's rule-based, threshold-driven decisions can be replaced by learned models that adapt to real network conditions. 

• Mine large-scale time-series and topology data (stored in Elasticsearch, InfluxDB, and MongoDB) to uncover patterns in network behavior at the scale of thousands of cells and large user populations. 

• Define and track quantitative success metrics so every model shipped can be proven to actually improve network outcomes, not just deployed and forgotten. 

• Present findings and roadmap recommendations to engineering and business leadership. 

What We're Looking For:

• 5+ years of applied data science / machine learning experience, including graph algorithms, clustering, anomaly detection, or time-series modeling. 

• Strong Python skills; comfort working alongside Go-based production services. 

• Experience with large-scale time-series and document data stores (Elasticsearch, InfluxDB, MongoDB, or comparable). 

• Experience with ML experiment tracking and deployment tooling (MLflow or equivalent) and with streaming data pipelines (Kafka or comparable). 

• Excellent communication skills — the ability to turn open-ended "why is the network behaving this way" questions into a shipped, measurable model. 

• Outstanding B.Sc. graduates in these fields may also be considered. 

• M.Sc. in Electrical Engineering, Computer Science, or Software Engineering.

 

Nice to Have:

• Background in telecom, RF, or wireless networking (handovers, KPIs such as RSRP or PRB utilization, cell topology). 

• Experience turning a hand-tuned, rule-based system into a learned model running in a live production environment. 

• Experience with distributed or streaming compute frameworks.