Mandatory requirements:

– Time series analysis at the telecom operator scale

– Experience with network traffic metrics (bandwidth, flows, protocols)

– SQL and time databases (Prometheus, Victoria Metrics)

– Building dashboards and visualizations for operational teams

– Statistical analysis and anomaly detection in network data

– Understanding of telecom metrics and KPIs of telecom operators

– Experience in analyzing subscriber behavior and service usage patterns

– Working with application classification (YouTube, Facebook, SSH, etc.)

– Understanding of QoS metrics and the impact of shaping/throttling policies

– Experience with data aggregation for long-term storage

Technical stack:

– Python for data analysis (taking into account compilation requirements)

– PromQL or similar query languages for time databases

– PostgreSQL + possible extensions for time series

– Experience with columnar storage for analytics

– Machine Learning and statistical modeling

– Real-time and batch data processing

Additional requirements:

– Readiness to work with closed systems without external dependencies

– Understanding of security requirements in the telecom industry

– Experience in international projects (the system is deployed in different countries)

– Knowledge of English (documentation, communication with DPI library vendors)

– Experience in managing data science projects

– Networking concepts (TCP/IP, RADIUS, GTP)

– Linux system configuration

– Git/GitHub for version control

A plus:

– Experience with DPI systems (Sandvine, Procera, Allot)

– Familiarity with DPDK and high-performance packet processing

– Experience with Rohde & Schwarz or Enea Qosmos SDK

– Understanding of network protocols and the structure of Internet traffic

– Experience with Dynamic Content Classification

– Working with Quality of Experience (QoE) models

– Network Intelligence and high-precision traffic classification

Education:

– BE/ME in Computer Science or Engineering

– 2+ years of practical experience in data science/ML