Join the forward-thinking CloudGuard AI Security team, where youll collaborate with leading architects and engineers to design, build, and secure next-generation AI-enabled public cloud environments.
As a Data Scientist, you will not only build and refine machine learning models and analyze large-scale datasets but also take an active part in the coding effort and in developing production-grade components that power CloudGuards AI Security products.
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, Code & Optimize ML Solutions Design, implement, and optimize ML and LLM-based models using clean, maintainable, and efficient code; integrate them into CloudGuards product pipelines to ensure scalability and reliability.
End-to-End Product Development Collaborate with backend and DevOps engineers to deploy, monitor, and scale ML components within microservices and cloud infrastructure.
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 production-ready, data-driven solutions.
As a Data Scientist, you will not only build and refine machine learning models and analyze large-scale datasets but also take an active part in the coding effort and in developing production-grade components that power CloudGuards AI Security products.
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, Code & Optimize ML Solutions Design, implement, and optimize ML and LLM-based models using clean, maintainable, and efficient code; integrate them into CloudGuards product pipelines to ensure scalability and reliability.
End-to-End Product Development Collaborate with backend and DevOps engineers to deploy, monitor, and scale ML components within microservices and cloud infrastructure.
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 production-ready, data-driven solutions.
Requirements:
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 (1-2 years recommended) using frameworks such as TensorFlow, PyTorch, scikit-learn, pandas and transformers libraries.
Programming Skills: Strong proficiency in Python (required) and familiarity with Go or other backend languages (advantageous). Ability to write production-level code, collaborate through Git, and work with CI/CD workflows.
Advantages:
Cloud Experience: Familiarity with AWS services (e.g., S3, Lambda, SageMaker, EKS/Kubernetes) for data storage, processing, and model deployment pipelines.
Analytical Mindset: Strong statistical background and the ability to derive insights from complex, large-scale datasets.
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/Python Development Experience: Experience contributing to production systems and collaborating closely with engineers.
Passion for AI Security: Eagerness to explore and tackle evolving challenges at the intersection of AI and cybersecurity.
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 (1-2 years recommended) using frameworks such as TensorFlow, PyTorch, scikit-learn, pandas and transformers libraries.
Programming Skills: Strong proficiency in Python (required) and familiarity with Go or other backend languages (advantageous). Ability to write production-level code, collaborate through Git, and work with CI/CD workflows.
Advantages:
Cloud Experience: Familiarity with AWS services (e.g., S3, Lambda, SageMaker, EKS/Kubernetes) for data storage, processing, and model deployment pipelines.
Analytical Mindset: Strong statistical background and the ability to derive insights from complex, large-scale datasets.
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/Python Development Experience: Experience contributing to production systems and collaborating closely with engineers.
Passion for AI Security: Eagerness to explore and tackle evolving challenges at the intersection of AI and cybersecurity.
This position is open to all candidates.
