Senior Data Scientist: Vulnerability Risk & ML Analytics

AgileEngine, LLC.; Porto; Híbrido; EUR 103 000 - 154 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Professional growth

Competitive compensation

Exciting projects

Flextime

Resumo da oferta

AgileEngine is seeking a Senior Data Scientist to develop predictive models and analytics that power risk-based vulnerability prioritization within a large-scale enterprise security program. You will structure and analyze massive security telemetry datasets, build ML algorithms to reduce scanning noise and false positives, and establish unified data dashboards to drive governance.

The role requires 5+ years of data science experience with direct commercial application to vulnerability management

Qualificações

• 5+ years of commercial data science experience with autonomous work.
• Advanced proficiency in Python, R, and SQL.
• Experience with machine learning frameworks such as TensorFlow and PyTorch.
• Experience with data visualization tools.
• Commercial experience applying data science to vulnerability management.

Responsabilidades

• Develop predictive models and quantitative analytics to prioritize vulnerabilities based on contextual risk and business impact.
• Structure and analyze massive outputs of security data from across the enterprise ecosystem to support the ASPM framework.
• Create machine learning algorithms or statistical models to automatically identify and filter out scanning noise and false positives.
• Establish a unified data delivery structure and dashboards to drive the adoption of data-driven security governance.

Conhecimentos

Data science

Python

R

SQL

Autonomy

Data visualization

Vulnerability management

Ferramentas

TensorFlow

PyTorch

Descrição da oferta de emprego

AgileEngine is seeking a Senior Data Scientist to develop predictive models and analytics that power risk-based vulnerability prioritization within a large-scale enterprise security program. You will structure and analyze massive security telemetry datasets, build ML algorithms to reduce scanning noise and false positives, and establish unified data dashboards to drive governance.

The role requires 5+ years of data science experience with direct commercial application to vulnerability management