Data Scientist

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 & Experience:

• BE/ME in Computer Science or Engineering
• 2+ years of practical experience in data science/ML