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