W
Senior Data Scientist - Network Intelligence
·Israel
HybridFull-timeData ScienceTelecommunicationsIoT
Analyze terabytes of telecom signaling, session, and usage data to build ML models for network selection, anomaly detection, forecasting, and cost optimization. Partner with Network Operations and Product to translate telemetry into actionable insights.
Responsibilities
- Design and deploy machine learning models on network data for network selection optimization, churn/usage prediction, fraud/anomaly detection, and QoS/QoE forecasting.
- Analyze CDRs, signaling events (Diameter, GTP, SIP), session logs, and radio metrics to identify performance issues, cost drivers, and growth opportunities.
- Partner with Network Operations and Product teams to translate raw telemetry into actionable signals for traffic steering and pricing.
- Build forecasting and segmentation models to inform pricing, capacity planning, and customer lifecycle strategy.
- Own end-to-end analyses: framing questions, exploring data, building and validating models, and communicating findings to technical and executive audiences.
- Define and track KPIs for network quality, customer experience, and commercial performance; build dashboards to make metrics visible.
- Mentor junior data scientists and analysts; improve data science practices, code quality, and experimentation rigor.
Requirements
- 5+ years of hands-on data science experience, ideally with telecom, networking, IoT, or large-scale event/log data.
- Strong applied ML background: classification, regression, time-series forecasting, anomaly detection, clustering.
- Expert SQL and strong Python (pandas, scikit-learn, PyTorch, or TensorFlow); comfort with big-data tooling (AWS Lakehouse, Redshift).
- Track record of shipping models into production and measuring business impact.
- Strong analytical storytelling: turn noisy data into clear narratives for non-technical stakeholders.
- Telecom and networking concepts: cellular architecture (2G/3G/4G/5G), roaming, IMSI/IMEI, HLR/HSS, signaling protocols, QoS metrics.
- Excellent written and spoken English.
Nice to have
- Familiarity with streaming data (Kafka, Flink) and real-time inference.
- Background in pricing, revenue management, or unit economics analysis.
- MSc or PhD in Computer Science, Statistics, EE, Physics, or related quantitative field.
Benefits
- Hybrid work
- Work-life balance
- Parking
- Medical insurance
- Food allowance
- Cellular plan
- Unlimited travel data plan
- Discount memberships
- Growth opportunities
- Professional development budget
- Global team collaboration
- Fun work environment and company activities
- On-site games (air hockey, pool table, ping-pong tournaments)