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We are looking for a strong, hands-on Data Scientist to join our Data Loss Prevention team.

In this role, you will join an R&D team that researches, designs, and develops Check Points data loss prevention cloud services, providing state-of-the-art AI-based data loss prevention solutions on a global scale.

Check Point's data loss prevention solution is a platform used by all Check Point products, including Network Security, Email Security, SASE, Endpoint, SaaS, and Cloud Security.

In today's AI-driven world, data is an invaluable asset, and safeguarding it is paramount.

This role offers a unique opportunity to leverage extensive data collections and deliver transformative results in the Data Loss Prevention domain.

Key Responsibilities


Join the research and development of new innovative capabilities for data loss prevention, involving advanced AI and algorithms.
Quickly iterate on design approaches based on data-driven research and user feedback.
Design and develop novel data-driven solutions that have the potential to deliver game-changing results involving advanced AI and algorithms.
Utilize Natural Language Processing (NLP) and large language models to enhance data loss prevention capabilities.
Work on web content categorization as part of the broader scope of data protection.
Collaborate with Cyber Researchers, Analysts, Fellow programmers, and Data Scientists to deliver machine learning and analytics solutions.
Push the solutions all the way to large-scale production systems. Understand the architectural constraints of such systems and work with a cross-organizational engineering and product team to quickly transition from prototype to a scalable robust implementation.

Join our AI & Innovation group as a Data Scientist and help shape the future of AI-driven security solutions. Youll work with a team of passionate, sharp-minded professionals tackling complex challenges at the intersection of AI, data science, and cybersecurity building the next generation of security products at the core of Check Points strategy.

Youll have access to some of the best cybersecurity data sources available, working in a fast-moving, dynamic environment that blends the agility of a startup with the scale and stability of a global leader.

If youre excited about solving real-world AI challenges, translating research into impactful solutions, and collaborating with top talent to push the boundaries of innovation we want to hear from you.

Key Responsibilities
Translate ambiguous product or business challenges into well-defined data science problems
Research and prototype cutting-edge AI techniques (LLMs, RAG, knowledge graphs, etc.)
Design and implement data-driven algorithms, analyze data, build and evaluate models
Collaborate with engineering to deploy scalable, production-ready solutions
Work with product teams to align AI solutions with user needs and business goals
Collaborate cross-functionally with engineers, product managers, and fellow scientists to bring research into production and deliver measurable impact
Stay current with advancements in AI/ML and generative AI, and actively contribute to innovation within the team and broader organization

We are looking for a strong, hands-on Data Scientist to join a cutting-edge AI and cyber security initiative at Check Point.
In this role, youll be part of a growing data science team working on advanced prevention technologies, leveraging vast amounts of data and state-of-the-art machine learning techniques to help protect users on a global scale.
This is a unique opportunity to join a high-impact, research-driven environment, where you will have the chance to shape core components of a next-generation security solution.

Key Responsibilities
Research and develop innovative AI-powered capabilities for cyber threat prevention.
Rapidly prototype and iterate based on data insights and user feedback.
Design and build data-driven solutions with a strong emphasis on practical impact and performance.
Collaborate closely with cyber researchers, analysts, and engineers to deliver production-grade machine learning models.
Work across organizational boundaries to bring solutions from concept to large-scale deployment, considering system constraints and integration challenges.