Label and annotate cybersecurity-related datasets to prepare them for analysis and machine learning tasks
Ensure labeling accuracy and consistency across different datasets, including threat intelligence data, incident reports, network logs, etc.
Gather data from various cybersecurity sources, including threat intelligence feeds, logs, and internal reports
Clean and preprocess data to make it suitable for analysis and modeling
Perform exploratory data analysis to uncover patterns, trends, and insights related to cybersecurity threats and vulnerabilities
Utilize statistical methods and tools to interpret data and identify potential security issues
Create and maintain dashboards and reports to communicate findings to cybersecurity stakeholders
Develop visualizations to present data in a clear and concise manner, highlighting key security metrics and trends
Work closely with analysts, data scientists, engineers, and other team members to support their data needs
Support the implementation and optimization of MLOps pipelines, leveraging data insights to deploy, monitor and scale machine learning models for different solutions
Participate in team meetings and contribute to project planning and discussions, providing data-driven insights
Document processes, methodologies, and insights gained from data analysis and labeling activities
Maintain clear records of data sources, cleaning steps, and labeling criteria to ensure reproducibility and auditability

Bachelor's degree in Computer Science or related STEM field
Proficiency in data manipulation and analysis tools (e.g., Python, SQL)
Familiarity with relevant libraries and frameworks (e.g., TensorFlow, PyTorch)
Experience with data labeling and annotation tools
Strong analytical and problem-solving skills, with an understanding of cybersecurity concepts
Excellent communication and collaboration abilities
Attention to detail and a commitment to data accuracy

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Paid parental and adoption leaves
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Data Analyst

Key skills