Data Scientist

Why Join Us?

Join the forward-thinking CloudGuard AI Security team, where you’ll collaborate with leading architects to design and secure next-generation AI-enabled public cloud environments. As a Data Scientist, you will build and refine machine learning models, analyze large-scale datasets, and create innovative solutions to address the evolving security challenges of AI adoption in modern enterprises.

Key Responsibilities
Develop AI Security Insights - Leverage advanced statistical and machine learning techniques to detect threats, anomalies, or vulnerabilities in AI-driven cloud environments.
Build and Optimize ML Models - Design, train, and evaluate models (including LLM-based solutions) for security-focused use cases, ensuring high performance and scalability.
Data Exploration & Feature Engineering - Conduct deep dives into complex datasets from various sources, transforming raw data into actionable features for robust model development.
Continuous Learning & Adapting - Stay current on the fast-paced AI landscape, enterprise use cases, and emerging threat vectors; adapt existing models and pipelines accordingly.
Cross-Functional Collaboration - Work closely with other data scientists, product architects, software engineers, and security experts to translate business needs into data-driven solutions and implement them into production pipelines.
Qualifications
B.Sc/M.Sc. or higher in Data Science, Computer Science, Statistics, Mathematics, or a related field.
Data Science & Machine Learning: Proven track record of building, testing, and deploying ML models (3+ years recommended).
Programming Skills: Proficiency in Python, with experience using ML/DL libraries such as TensorFlow, PyTorch, or scikit-learn.
Cloud Experience: Familiarity with AWS services, especially for data storage, processing, or MLOps workflows (bonus points for EKS/Kubernetes).
Analytical Mindset: Strong statistical background and the ability to derive insights from complex, large-scale datasets.

Advantages:

LLM Expertise: Hands-on experience with large language models, vector databases, or related NLP frameworks.
Security Knowledge: Understanding of cybersecurity concepts, threat detection, and best practices for secure ML pipelines.
GO/C++ Experience: While not mandatory, experience with these languages could help when collaborating on integrated systems.
Passion for AI Security: Eagerness to explore and tackle evolving challenges at the intersection of AI and cybersecurity.