Sobre este puesto de Senior Data Scientist — Network Intelligence en Parallelwireless
What You'll Do:
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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..
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Build a real, repeatable experimentation and model-deployment workflow (e.g., using MLflow or comparable tooling), taking models from notebook to production.
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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.
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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.
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Define and track quantitative success metrics so every model shipped can be proven to actually improve network outcomes, not just deployed and forgotten.
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Present findings and roadmap recommendations to engineering and business leadership.
What We're Looking For:
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5+ years of applied data science / machine learning experience, including graph algorithms, clustering, anomaly detection, or time-series modeling.
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Strong Python skills; comfort working alongside Go-based production services.
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Experience with large-scale time-series and document data stores (Elasticsearch, InfluxDB, MongoDB, or comparable).
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Experience with ML experiment tracking and deployment tooling (MLflow or equivalent) and with streaming data pipelines (Kafka or comparable).
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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.
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M.Sc. in Electrical Engineering, Computer Science, or Software Engineering.
Nice to Have:
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Background in telecom, RF, or wireless networking (handovers, KPIs such as RSRP or PRB utilization, cell topology).
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Experience turning a hand-tuned, rule-based system into a learned model running in a live production environment.
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Experience with distributed or streaming compute frameworks.
