דרושים » דאטה » data Scientist בחברת הייטק אשר מפתחת פלטפורמת Market Place

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

As a Data Engineer at Meta, you will shape the future of people-facing and business-facing products we build across our entire family of applications (Facebook, Instagram, Messenger, WhatsApp, Reality Labs, Threads). Your technical skills and analytical mindset will be utilized designing and building some of the world's most extensive data sets, helping to craft experiences for billions of people and hundreds of millions of businesses worldwide.

In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across Meta to optimize growth, strategy, and user experience for our 3 billion plus users, as well as our internal employee community.

You will be at the forefront of identifying and solving some of the most interesting data challenges at a scale few companies can match. By joining Meta, you will become part of a world-class data engineering community dedicated to skill development and career growth in data engineering and beyond.

Data Engineering: You will guide teams by building optimal data artifacts (including datasets and visualizations) to address key questions. You will refine our systems, design logging solutions, and create scalable data models. Ensuring data security and quality, and with a focus on efficiency, you will suggest architecture and development approaches and data management standards to address complex analytical problems.

Product leadership: You will use data to shape product development, identify new opportunities, and tackle upcoming challenges. You'll ensure our products add value for users and businesses, by prioritizing projects, and driving innovative solutions to respond to challenges or opportunities.

Communication and influence: You won't simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.
Data Engineer, Product Analytics Responsibilities
Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way
Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
Define and manage Service Level Agreements for all data sets in allocated areas of ownership
Solve challenging data integration problems, utilizing optimal Extract, Transform, Load (ETL) patterns, frameworks, query techniques, sourcing from structured and unstructured data sources
Improve logging
Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts
Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts
Influence product and cross-functional teams to identify data opportunities to drive impact