Description

Senior Data Scientist with 8+ years across telecom, cybersecurity, and energy.

At Anritsu (Innovation team), I build production-grade GenAI and agentic AI systems for the telecom industry, most recently an agentic diagnosis system (LangGraph, RAG over a Neo4j network graph, live telemetry) that autonomously investigates subscriber QoE issues, cutting investigation time from hours to minutes.

Also, I design AI use cases end-to-end: from architecture and prototyping to deployment and demos in front of operator clients, such as: Fraud detection (~89% precision), NPS prediction, and customer analytics.

What I work with:

- GenAI & Agentic AI: LangGraph, RAG, Langfuse, Ragas, Vertex AI

- ML & MLOps: Python, MLflow, Docker, Kubernetes, Airflow, AWS

- Telecom analytics: CEM/CX scoring, QoE diagnosis, sentiment analysis, segmentation

- Data engineering: Spark, Kafka, Neo4j, pgvector, ClickHouse

I own projects end-to-end - prototype to production - and I'm as comfortable in front of a client as in the codebase.

Open to exchanging on GenAI/agentic projects in telecom and beyond. Feel free to reach out.

Industry field of expertise

Languages

Arabic
Native or bilingual
French
Native or bilingual
English
Fluent
German
Basic

Workplace preferences

Remote only

Primarily works remotely

Anritsu
Senior Data Scientist - Innovation Team
TELECOMMUNICATIONS
March 2023 - Today (3 years and 4 months)
Rennes, France
- Designed the AI logic behind a next-gen CEM / CX scoring solution, including the CEM Investigator Agent - an agentic system (RAG over a Neo4j network graph, live telemetry) that autonomously diagnoses subscriber QoE issues, cutting investigation from hours to minutes.
- Shipped an end-to-end AI sentiment analysis product for telecom operators: LLM-based scoring, topic classification, and competitor benchmarking over complaints from 6 sources, with autogenerated reports. Deployed across 4 operators.
- Created a customer segmentation model from mobile-app usage behavior, surfacing 4-6 actionable segments for marketing targeting.
- Collaborated with customers and supported the sales team by presenting and demonstrating AI
solutions.

Tools: Agentic AI, LangGraph, RAG, Neo4j, pgvector, Vertex AI, Langfuse, LangSmith, Ragas, MLflow, Airflow, Docker, Kubernetes, AWS, Vertica, Spark.
Python AWS Docker MLOps Agentic AI
Falcon Project
Co-founder & Technical Lead [Side Project]
HOSPITALITY
February 2023 - September 2025 (2 years and 7 months)
Falcon Project: B2B SaaS BI platform that transforms public consumer reviews into actionable
market insights for SMEs.

In this role I:
- Led the E2E backend engineering and cloud infrastructure (~600 businesses).
- Developed the core intellectual property by designing custom AI models and processing reviews from 5 sources.
- Directed team workflows and agile project management (4 team members).
artificial intelligence Python Docker
iGrafx
Data & ML Engineer
DIGITAL AND IT
September 2022 - February 2023 (5 months)
Rennes, France
- Developed and productionized GNN models to optimize business process efficiency.
- Enhanced data pipelines using Distributed Computing Frameworks to improve data quality, scalability, and robustness, reducing processing time by ~50%.

Tools: Python, GNN, Ray, MLflow, Docker.
GNN Python MLOps Machine learning Docker