About Webbing:

Founded in early 2010, Webbing is a global data MVNO that delivers enterprise grade, global connectivity and IoT services across more than 200 countries and 600+ mobile carriers' networks. Webbing's secured network delivers network protection and web content intelligence.

The Opportunity:

We're looking for a Data Scientist to turn our network data into a competitive advantage. You'll work directly with terabytes of telecom signaling, session, and usage data to build models that improve network selection, detect anomalies, optimize cost-per-gigabyte, and surface insights that shape product and commercial strategy. This is a high-impact role sitting at the intersection of ML, telecom engineering, and business decision-making.

What You'll Do:

• Design and deploy machine learning models on network data — including network selection optimization, churn and usage prediction, fraud and anomaly detection, and QoS/QoE forecasting.
• Analyze CDRs, signaling events (Diameter, GTP, SIP), session logs, and radio-level metrics to identify performance issues, cost drivers, and growth opportunities.
• Partner with the Network Operations and Product teams to translate raw telemetry into actionable signals — e.g., which carrier to steer traffic to in a given country at a given hour.
• Build forecasting and segmentation models that inform pricing, capacity planning, and customer lifecycle strategy.
• Own analyses end-to-end: framing the business question, exploring the data, building the model, validating it, and communicating findings to both technical and executive audiences.
• Define and track KPIs for network quality, customer experience, and commercial performance, and build dashboards that make those metrics visible across the company.
• Mentor junior data scientists and analysts, and help raise the bar on data science practices, code quality, and experimentation rigor.

• Requirements:
• 5+ years of hands-on data science experience, ideally with exposure to telecom, networking, IoT, or large-scale event/log data.
• Strong applied ML background: classification, regression, time-series forecasting, anomaly detection, clustering, and a working understanding of when to use what.
• Expert SQL and strong Python (pandas, scikit-learn, PyTorch, or TensorFlow). Comfort with big-data tooling such as AWS Lakehouse, Redshift, and others.
• Track record of shipping models into production and measuring their business impact, not just building notebooks.
• Strong analytical storytelling — you can take a noisy dataset and turn it into a clear narrative for a non-technical stakeholder.
• Telecom and networking concepts — cellular architecture (2G/3G/4G/5G), roaming, IMSI/IMEI, HLR/HSS, signaling protocols, QoS metrics. If you don't have this yet but have worked on similarly complex network/log data, we'd still like to talk.
• 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 a related quantitative field.

What we offer:

• Hybrid work
• Work life balance
• Parking
• Medical insurance
• Food allowance
• Cellular plan
• Unlimited data plan for traveling
• Discount memberships
• Growth opportunities
• Professional development budget
• Global company- working and collaborating with amazing people around the world
• Fun working environment & company activities
• And of course, air hockey, pool table, and ping-pong tournaments!