Senior ML/AI Specialist (K/M)

NASK SCIENCE, a division of NASK, conducts research and development in cybersecurity, computer science, and artificial intelligence. The role involves practical work with data and machine learning models in network traffic analysis, covering the entire data workflow from acquisition and preparation to model implementation, experimentation, and evaluation. The goal is to identify, design, and assess AI and ML approaches for detecting anomalies, threats, and unusual patterns in network data.

• Minimum 3 years of experience in machine learning, deep learning, and data analysis
• Advanced knowledge of fundamental machine learning algorithms
• Practical experience in training and evaluating ML/AI models
• Ability to work with time series or sequential data
• Ability to select methods appropriate to the problem
• Critical assessment skills for model effectiveness and stability
• Understanding of ML/AI model limitations and risks
• Higher education in a technical field
• English proficiency sufficient for reading technical documentation
• Advanced knowledge of Python and ML libraries (Tensorflow and/or Pytorch, numpy, scikit-learn, pandas)
• Willingness to work iteratively and experimentally
• Teamwork skills and ability to communicate results

Nice to have:

• Experience in research and development or exploratory projects
• Experience in cybersecurity or network traffic/telemetry data analysis
• Experience working with time series and sequential data
• Knowledge of anomaly detection or monitoring systems

• Prepare and analyze data related to network traffic
• Analyze anomaly detection problems in network traffic from ML/AI perspectives
• Select features and data representations
• Choose model classes and techniques appropriate to data characteristics
• Develop concepts and prototypes of ML/AI solutions
• Implement and train ML/AI models for anomaly detection
• Evaluate effectiveness, applicability, and limitations of models and methods
• Analyze data drift resilience
• Analyze errors, risks, and false positive detections
• Collaborate on building data processing and model training pipelines
• Cooperate with AI, architectural, and system teams
• Document experiment results and observations

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