Are you a data whiz with a passion for cybersecurity? Do you crave challenges that involve translating complex data into actionable insights to combat cyber threats? Then Safe Security has the perfect opportunity for you!
Safe Security is a leading provider of AI-driven cyber risk management solutions. Our real-time, data-driven platform empowers organizations to predict, prevent, and quantify cyber breaches. As a Data Scientist , Cyber Risk, you will play a critical role in our research team, leveraging your expertise to extract valuable insights from security data and develop models to strengthen our risk assessment capabilities.
Core Responsibilities
- Data Acquisition and Wrangling:
- Collaborate with security engineers and IT professionals to collect, extract, and transform relevant data from various sources, including threat intelligence feeds and third-party data.
- Ensure data quality and integrity through cleaning, normalization, and feature engineering techniques.
- Cyber Risk Modeling and Quantification:
- Develop and apply statistical and quantitative models to assess cyber threats' likelihood and potential financial impact.
- Contribute to developing risk mitigation strategies by identifying and prioritizing high-risk areas.
- Build automations to simulate different scenarios.
- Visualization and Communication:
- Create clear and compelling visualizations to communicate complex cyber risk insights to technical and non-technical stakeholders.
- Present findings and recommendations effectively to inform decision-making on security investments and resource allocation.
- Stay Current and Innovate:
- Maintain awareness of the latest advancements in cyber risk research, data science, and machine learning techniques.
- Proactively propose and champion innovative solutions to enhance our cyber risk management practices.
Qualifications
- Degree in Computer Science, Statistics, Data Science, or a related field (or equivalent experience).
- Minimum 1-3 years of experience in data science, machine learning, or a relevant domain.
- Strong understanding of statistical analysis, data modeling techniques, and machine learning algorithms (e.g., supervised learning, anomaly detection).
- Experience with programming languages like Python and R, ideally with frameworks like Scikit-learn, TensorFlow, or PyTorch.
- Experience working with big data platforms or cloud technologies (AWS, Azure, GCP) is a plus.
- Excellent communication and collaboration skills.
- A keen interest in cybersecurity and a strong understanding of cyber risk concepts.
Bonus points for:
- Experience in developing machine learning models for security applications (e.g., intrusion detection, malware analysis).
- Experience working with threat intelligence feeds, security information, and event management (SIEM) systems.
- Familiarity with cyber risk quantification methodologies (e.g.,MITRE, FAIR).
