As a Principal Data Scientist, you will apply your expertise in data analysis, machine learning, and cybersecurity to build new AI-based defense techniques against cyber-threats. You will be a driving force in designing and developing groundbreaking, ML-based security solutions that block attacks even before they begin, shaping the future of cybersecurity and impacting millions of customers.
Key Responsibilities
Contribute to a diverse and highly skilled research group that pioneers state-of-the-art technologies to protect our customers.
Utilize analytical rigor, statistical methods, programming, and data modeling to analyze vast amounts of data, applying your cybersecurity knowledge to guide our focus and approach.
Decompose complex problems scientifically and provide actionable insights and recommendations to both technical and non-technical stakeholders.
Lead and collaborate on end-to-end projects within the team and across engineering and product teams, from initial ideation to final deployment.
Key Responsibilities
Contribute to a diverse and highly skilled research group that pioneers state-of-the-art technologies to protect our customers.
Utilize analytical rigor, statistical methods, programming, and data modeling to analyze vast amounts of data, applying your cybersecurity knowledge to guide our focus and approach.
Decompose complex problems scientifically and provide actionable insights and recommendations to both technical and non-technical stakeholders.
Lead and collaborate on end-to-end projects within the team and across engineering and product teams, from initial ideation to final deployment.
Requirements:
6+ years of hands-on experience delivering production-grade data science and machine learning projects.
Proven track record in applied data science for cybersecurity.
Deep expertise in probability, statistics, and machine learning, with a proven ability to select, adapt, and apply advanced algorithms to real-world problems.
Proven deep learning experience, including model design, training, and evaluation.
Strong proficiency in Python with solid working knowledge of SQL.
Advanced degree (MSc or PhD) in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Mathematics, or a closely related field.
Demonstrated ownership of end-to-end research POCs, from problem formulation and experimental design through execution, analysis, and final recommendations.
Strong emphasis on engineering-quality, production-ready code with high standards for reliability, scalability, and maintainability.
Excellent communication skills, with the ability to clearly articulate complex technical insights to both technical and business stakeholders.
Preferred Qualifications
Direct experience with detection and response platforms (XDR, EDR, or NDR).
Experience working with Big Data platforms (e.g., GCP, BigQuery, Dataflow).
Familiarity with cloud-native architectures and MLOps tools for managing the model lifecycle at scale.
6+ years of hands-on experience delivering production-grade data science and machine learning projects.
Proven track record in applied data science for cybersecurity.
Deep expertise in probability, statistics, and machine learning, with a proven ability to select, adapt, and apply advanced algorithms to real-world problems.
Proven deep learning experience, including model design, training, and evaluation.
Strong proficiency in Python with solid working knowledge of SQL.
Advanced degree (MSc or PhD) in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Mathematics, or a closely related field.
Demonstrated ownership of end-to-end research POCs, from problem formulation and experimental design through execution, analysis, and final recommendations.
Strong emphasis on engineering-quality, production-ready code with high standards for reliability, scalability, and maintainability.
Excellent communication skills, with the ability to clearly articulate complex technical insights to both technical and business stakeholders.
Preferred Qualifications
Direct experience with detection and response platforms (XDR, EDR, or NDR).
Experience working with Big Data platforms (e.g., GCP, BigQuery, Dataflow).
Familiarity with cloud-native architectures and MLOps tools for managing the model lifecycle at scale.
This position is open to all candidates.
