Cyberark Data Scientist Team Leader 

Israel 

941884563

As a Data Science Team Leader at CyberArk, you will lead a high-performing team developing AI/ML solutions that secure the digital world’s most sensitive assets. You’ll play a key role in driving innovation, productizing machine learning systems, and applying advanced modeling techniques to real-world cybersecurity challenges. In this leadership role, you’ll balance hands-on technical involvement with strategic oversight, guiding projects from research to production. You’ll work closely with data scientists, engineers, analysts, product managers, and Cyber-Security researchers to build intelligent features across CyberArk’s identity security portfolio.

• Lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions.
• Own the end-to-end development lifecycle of AI projects - from exploratory research and experimentation through scalable deployment and optimization.
• Partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
• Drive innovation across a broad spectrum of ML domains including supervised/unsupervised learning, anomaly detection and generative AI.
• Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
• Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
• Champion best practices in MLOps, data governance, explainability, and monitoring.
• Keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.

• 6+ years of industry experience in AI/ML or Data Science, with at least 1-year leading teams and managing direct reports.
• Master’s degree or PhD in a technical field (e.g., Computer Science, Machine Learning, Statistics, Engineering).
• Solid proficiency in Python and machine learning libraries/frameworks such as scikit-learn, PyTorch, TensorFlow, or Hugging Face.
• Strong familiarity with natural language processing (NLP), including LLMs and transformer-based architectures, and their applications to real-world use cases.
• Strong technical communication skills and the ability to collaborate across cross-functional teams.
• Strategic mindset with the ability to connect AI research to business value and product opportunities.

• Familiarity with cybersecurity, identity security, or risk mitigation use cases.
• Experience with cloud-based ML infrastructure (e.g., AWS SageMaker, GCP Vertex AI) and big data tools (e.g., Spark, Airflow).
• Hands-on knowledge of MLOps tooling for CI/CD, monitoring, and versioning of ML assets.
• Publications, patents, or open-source contributions in AI/ML.