דרושים Lead Data Scientist בתל אביב

A research and development company specializing in cyber research, working on cutting-edge projects for Israel’s defense and intelligence community.

The company is characterized by excellence profiles graduates of elite technological units and top experts from academia and industry all driven by a strong sense of mission, impact, and national purpose.

Located in Herzliya near the train station and have about 250 employees.

• Role overview-
• Personal and professional management of a team of 2–3 Data Scientists.
• Approximately 50% hands-on work in ML/LLM model development.
• Reporting directly to the Group Lead as part of a newly created management layer.
• Analysis, development, and deployment of Machine Learning and LLM-based models.
• Work with diverse data types: graphs, structured data, text, and images.
• Technical and strategic leadership, including defining technological direction and long-term vision.
• Development of production-ready ML/DL models and pipelines.
• Leading integration of models into existing engineering and cyber systems.
• Coding in Python using scikit-learn, PyTorch, pandas, and related libraries.
• Work with tools and environments such as Kubernetes, Spark, Elasticsearch, and Airflow.
• Close collaboration with Product teams and end customers.
• Leading ML processes end-to-end: from research, experimentation, and modeling to deployment and monitoring.
• Work across a broad tech stack including ML/DL, LLM, RAG, Graph Data, Structured Data, Text Processing, Image Data, Data Science, and Cybersecurity.

Requirements-

• 2 years of management experience — required (flexible, aiming for 3+).
• 4 years of experience as a Data Scientist — required.
• Proven experience with ML, DL, and LLMs — required.
• Familiarity with Kubernetes, Spark, Elasticsearch, and Airflow — required.
• Experience working with graph data — an advantage.
• Experience in cybersecurity domains — an advantage.
• Experience with large-scale graphs and Big Data — an advantage.
• Experience in fine-tuning LLMs or building end-to-end RAG agents — an advantage.