Cybersecurity Data Scientist

Eviden, part of the Atos Group, with an annual revenue of circa €5 billion is a global leader in data-driven, trusted and sustainable digital transformation. As a next generation digital business with worldwide leading positions in digital, cloud, data, advanced computing and security, it brings deep expertise for all industries in more than 47 countries. By uniting unique high-end technologies across the full digital continuum with 47,000 world-class talents, Eviden expands the possibilities of data and technology, now and for generations to come.

Join our dynamic Cybersecurity R&D Team and play a pivotal role in shaping the future of information and data security technologies. As a Penetration Testing Engineer, you will contribute to the design, development, and implementation of cutting-edge cybersecurity solutions across multiple vertical scenarios and technologies. Areas of work include pentesting automation, vulnerability management, incident detection and response, and research on the application of technologies such as artificial intelligence to solve cybersecurity challenges. As part of the Cybersecurity R&D Team, you will be involved in the design, definition of system architectures, software development, prototype testing and piloting of cybersecurity solutions. In your daily work you will be responsible for driving, designing, and building cutting edge innovation in the space of cybersecurity through Artificial Intelligence and Machine Learning - current scope of problems includes pentesting & security testing automation, behavioural analysis and the protection with and against AI driven technologies. You will be using your core competencies around AI and data science and help drive the team to build models and solutions to be applied into cybersecurity products and services.

Basic required skills :

• Skilled in Artificial Intelligence, Machine Learning, Deep Learning, and Data Science techniques.
• Deep knowledge of Reinforcement Learning, including Markov Decision Processes (MDPs) modelling and Q-learning, Deep Q-Learning (DQN).
• Deep understanding of statistics and analysis.
• Ability to code in multiple languages, including Python.
• Experience with data transformation (structured data format / schema transformation) using common programming tools (e.g., Python, JSON, etc.).
• Experience with ML frameworks, including Keras, Pandas, TensorFlow, or Pytorch.
• Experience with Git and collaboration tools, including Jenkins, JIRA, Confluence, Nexus, or Bitbucket.
• Fluent spoken and written English.
• Self-starter, proactive and autonomous character.
• Ability to take part in technical discussions / negotiation among multiple actors.

Other valuable skills :

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