Senior Data Scientist — Network Intelligence - Parallel Wireless

Parallel Wireless is reimagining mobile networks with innovative, energy-efficient Open RAN solutions. Join us as we lead the future of telecommunications, driving innovation through green and sustainable networks. Learn more about our mission, vision and values.

We're hiring a Senior Data Scientist to own the non-real-time, ML-driven side of that system: turning large-scale network telemetry into models and analysis that make the entire platform smarter over time. You'll work directly on the algorithms that cluster cells, detect conflicts, and forecast load — and you'll partner closely with the real-time engineering team to push more of that intelligence into automated decision-making.

Parallel Wireless is expanding the ecosystem for Open RAN with the GreenRAN™ energy-efficient Hardware-Agnostic technology. Deployed worldwide, our comprehensive 2G/3G/4G/5G Macro RAN solutions enhance network security while reducing operating expenses. As pioneers of Open RAN, we prioritize innovation, flexibility, and sustainability to help build a more connected, and green networks. Headquartered in the USA with global R&D centers, we are proud to serve over 60 customers worldwide and have been recognized with over 100 industry awards. Our mission is to accelerate GSMA’s Mobile Net Zero initiative by reducing TCO and driving innovation across the telecom ecosystem.Learn . Parallel Wireless embraces diversity and equality of opportunity. We are committed to building inclusive and diverse teams representing all backgrounds, with a wide range of perspectives, and empowering industry-leading skills. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.Parallel Wireless does not accept unsolicited resumes or applications from agencies or individuals. Please do not forward resumes to our jobs alias, Parallel Wireless employees, or any other company location. Parallel Wireless is not responsible for any fees related to unsolicited resumes/applications.

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.

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. 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.