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.

Similarweb is the leading digital intelligence platform used by over 3500 global customers. Our wide range of solutions power the digital strategies of companies like Google, eBay, and Adidas.

We help our customers succeed in todays digital world by giving them access to data-driven insights, competitive benchmarks, strategic analysis, and more.

In 2021, we went public on the New York Stock Exchange, and we havent stopped growing since!

Were looking for a Data Scientist to join our Data Labs department, which specializes in professional services for our super premium customers.

This role will report to our Datalabs Team Manager in the, R&D.


Why is this role so important at Similarweb?

Similarweb is a data-focused company, and our unique AI and machine learning capabilities are the center of our business.

As part of this role, you will create data models that help analyze the petabytes of data we receive from various sources, and research and develop new features and capabilities for our product solutions

As a data scientist in the Datalabs team, you will work on the very core of the company. Part of your role will be to turn raw data into usable metrics and develop new models and statistical algorithms to support out-of-the box requests from customers who want custom digital insights. The Datalabs departments customer centric and business-oriented nature also means you will be supporting a team of data analysts and consultants who interact directly with customers. Together with them, you will translate the voice of these customers into best-in-class digital insights.

So, what will you be doing all day?

Your role as part of the R&D department, and as part of the Datalabs team means your daily responsibilities may include:

Working on versatile use cases within a dynamic, fast paced production environment
Strategic thinking and problem solving, with a strong business sense
Analyzing data from various sources, and turning it into usable input
Understanding various use cases from different customers, and creating models that can translate these cases into meaningful insights
Initiating newly defined tasks from exploration to proof of concept
Owning the finalization of defined tasks (from POC to deployment in production)
Collaborating with data engineers to make sure we reach the desired production results
Owning the ongoing validation process for existing metrics
Collaborating and brainstorming best practices with other teams within the R&D department
Deploying machine-learning models into production for an ongoing use