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Data Scientist

Job Description

At Thales, we know technology has the ability to make our world more secure, sustainable, and inclusive – and that it’s all driven by human intelligence.

Because it takes human intelligence to build and power the systems and solutions that people depend on every day. So we stay curious and make space for diverse points of view. We share what we know and we challenge what’s possible.

We’re driving progress in some of the world’s most important industries - from the bottom of the oceans to the depths of space and cyberspace - and from manufacturing to engineering, we work together to build a future we can all trust.

Imperva, a Thales company, is a globally recognized cybersecurity leader, dedicated to securing data and applications across diverse environments. Our cutting-edge solutions empower organizations to safeguard their most critical assets, ensuring robust protection against emerging threats.

Thales believes that technology becomes truly powerful when driven by human intelligence. We value curiosity, diverse perspectives, and bold thinking—and we are proud to be ranked among Israel’s Top 50 High‑Tech Companies to Work For in 2025 (Dun & Bradstreet) and offer a flexible hybrid work model from our Tel Aviv office.

We are looking for a highly technical and innovative, hands-on Data Scientist to join our Threat Research group.

Our Threat Research group is composed of elite researchers and developers. We research applications, DDoS, and database attacks, develop algorithms for new products, and drive innovation and thought leadership in cybersecurity.

Key Responsibilities:

  • Lead and contribute to the development, evaluation, and deployment of machine learning, deep learning, and large language models (LLMs) for cybersecurity applications.

  • Drive and support data-driven research projects, from ideation and prototyping to production deployment, using both classic ML and advanced deep learning techniques.

  • Perform comprehensive data collection, preprocessing, analysis, and feature engineering on large-scale security datasets.

  • Develop and optimize innovative security solutions for integration into Imperva products.

  • Collaborate closely with data scientists, security researchers, engineers, and product teams to deliver impactful solutions.

  • Actively participate in knowledge sharing, code reviews, and continuous team learning.

Requirements:

  • 2 - 4 years of industry experience in data science or machine learning, with hands-on involvement in real-world projects - internships, academic projects, or open-source contributions are highly valued.

  • Proven experience developing and deploying ML models (Python preferred).

  • Proficient understanding and hands-on experience with neural networks, deep learning, and LLMs, as well as classic machine learning techniques.

  • Strong experience in data analysis, manipulation, and transformation using tools such as Pandas, NumPy, and related libraries.

  • Familiarity with ML/DL frameworks (e.g., PyTorch, TensorFlow, Keras, Hugging Face).

  • Experience with cloud platforms, Kubernetes, microservices, or CI/CD is a plus.

  • Background in application or data security is a strong advantage.

  • A degree in a quantitative field is valued but not required with strong relevant experience.

  • Excellent teamwork, communication skills, and a strong willingness to learn and innovate.

Thales, champions inclusion and we believe diversity strengthens the fabric of our culture. We are an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, colour, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.
 

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Job Details

Category
Software
Employment Type
Full Time
Location
TEL AVIV
Posted
Mar 18, 2026, 08:00 PM
Listed
Mar 5, 2026, 10:15 PM

About Thales Alenia Space

Part of the growing space & AI ecosystem pushing the frontiers of technology.

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