Senior Data Analyst

Job Profile:

Analyze large datasets related to operational technology (OT) security vulnerabilities and develop data-driven insights to enhance cybersecurity products and solutions. Drive data quality improvements and conduct analytical experiments to strengthen vulnerability management systems.

Key Responsibilities:

Analyze large datasets related to OT security vulnerabilities and threat intelligence

Develop data-driven insights and recommendations to improve cybersecurity products

Ensure data quality for vulnerability databases and recommend systematic improvements

Conduct experiments and statistical analysis to improve data quality and accuracy

Design and implement automated data processing workflows for security datasets

Create comprehensive reports and visualizations for stakeholders and management

Collaborate with security teams to identify patterns and trends in vulnerability data

Support the development of predictive models for threat assessment and risk analysis

Maintain and optimize data pipelines for real-time security monitoring

Desired Qualification and Experience:

Education: Bachelor's or Master's degree in Engineering - Computer Science, Data Science, Cybersecurity, Information Technology, or related field

Experience: 8-10 years of professional experience working as a data analyst in data-driven projects with focus on security or cybersecurity domains

Technical Skills:

Data Analysis: Strong proficiency in Python, R, SQL, and data analyst toolset for large-scale data processing

Security Domain: Deep understanding of security and vulnerability standards (CVE, CVSS, CWE, NIST frameworks)

Automation: Experience in automation of API-based systems and data integration workflows

Database Technologies: Proficiency in working with vulnerability databases, threat intelligence feeds, and security data repositories

Visualization: Experience with data visualization tools (Tableau, Power BI, matplotlib, seaborn)

Statistical Analysis: Strong foundation in statistical methods, hypothesis testing, and experimental design

Network Protocols: Knowledge of network discovery protocols and OT/IT network architectures

Asset Management: Understanding of asset description norms, formats, and industrial control system standards

Domain Knowledge:

OT Security: Understanding of operational technology environments, industrial control systems (ICS/SCADA), and OT-specific vulnerabilities

Vulnerability Management: Experience with vulnerability assessment processes, threat modeling, and risk prioritization

Compliance Standards: Knowledge of cybersecurity frameworks and standards (ISO 27001, NIST, IEC 62443)

Threat Intelligence: Understanding of threat intelligence feeds, indicators of compromise (IoCs), and threat landscape analysis

Collaboration and Soft Skills:

Stakeholder Engagement: Openness to collaborate with diverse teams including P-CERT, BSI (federal agency), customers, Siemens employees (product owners, experts), and standardization organizations

Communication: Excellent written and verbal communication skills to present complex data insights to technical and non-technical audiences

Global Collaboration: Ability to work effectively in global, distributed teams across different time zones

Adaptability: Flexibility to work with evolving security threats and changing regulatory requirements

Project Management: Ability to manage multiple data analysis projects simultaneously and meet deadlines

Preferred Qualifications:

Certifications: Relevant cybersecurity certifications (CISSP, CISM, CEH) or data science certifications

Industry Experience: Previous experience in industrial cybersecurity, critical infrastructure protection, or OT environments

Research Background: Experience in security research, vulnerability research, or academic publications in cybersecurity

Cloud Platforms: Experience with cloud-based data analytics platforms (AWS, Azure, Google Cloud)

Machine Learning: Knowledge of machine learning techniques applied to cybersecurity use cases