Cybersecurity Data Scientist

Key Skills

Aartificial intelligence, Agile methodologies, Cyber Security, Data Modeling, Data science techniques, Deep Learning, DevSecOps, Effective communication skills, Git, Jenkins, JIRA, Keras software library, Kubernetes-K8s, Machine learning techniques, Pandas, Python Programming, Statistics, TensorFlow, XML-JSON

Job Description

The position involves research in technology for information and data security to be applied to cybersecurity products and services.

In this position you will work on software design and development of prototypes with industrial application based on recent research and new concepts in cybersecurity, for application in multiple vertical scenarios and across technologies. Areas of work include pentesting automation, threat intelligence, incident detection and response and research on the application of technologies such as artificial intelligence to solve challenges in cybersecurity. As part of the cybersecurity research team you will take part in the design of the cybersecurity solutions, definition of system architectures, development of software, prototype testing phases, and piloting of solutions. You will work under the direction of the Cybersecurity Research Unit manager. 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:

- Expertise in cybersecurity concepts and technologies.

- Expertise in Kubernetes platform security and DevSecOps methodologies.

- Experience with data modelling for graphs and graph analysis.

- Experience / knowledge in distributed systems, internet communication protocols.