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.
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.
At least 3-4 years of relevant experience and track record in Data Science: Machine Learning, Deep Learning, and Statistical Data Analysis.
Proven experience in developing and deploying production-level models within enterprise environments.
MSc or PhD degree in CS or Mathematics, Bioinformatics, Statistics, Engineering, Physics, or similar discipline.
Strong hands-on experience in Python with a focus on statistical algorithms development.
Experience with data science libraries such as: sklearn, pandas, numpy, pytorch/tensorflow.
Team player, able to work in collaboration with subject matter experts, with the ability to clearly present and communicate findings.
Proven ability to build and deliver data solutions in a short time frame.
Experience with AWS, Docker, and development methodologies – an advantage.
Active Github, Kaggle member an advantage.
Background in data loss prevention or related fields an advantage.
