Verizon is looking to identify and neutralize threats using advanced data analytics and anomaly detection to safeguard the company's information and systems.
Requirements
Proven experience designing and implementing scalable AI/ML models for production, specifically for anomaly detection, classification, and prediction.
Programming & Data Science experience: Expert proficiency in Python and its data science ecosystem (Scikit-Learn, Pandas, NumPy).
Deep technical understanding of machine learning and deep learning architectures (e.g., Transformers, Autoencoders).
Machine Learning Frameworks: Hands-on experience with TensorFlow or PyTorch.
Big Data & Engineering: Experience with SQL and distributed data processing frameworks (Spark or Dask).
Cloud & MLOps: Proficiency with cloud platforms (GCP or AWS), containerization (Docker), version control (Git), and MLOps tools (MLFlow).
Cybersecurity Expertise: Strong understanding of core cybersecurity principles, threat vectors, and frameworks like MITRE ATT&CK.
Responsibilities
Developing Advanced Threat Detection Models: Design, build, and deploy production-grade machine learning models for threat detection, vulnerability assessment, and behavioral anomaly analysis using large-scale security datasets.
Conducting In-Depth Data Analysis: Perform exploratory data analysis on diverse security data sources (e.g., SIEM logs, network traffic, endpoint data) to uncover hidden patterns, correlations, and insights that inform our cybersecurity strategy.
Collaborating and Integrating Solutions: Work closely with cybersecurity analysts and engineers to integrate ML models into our security framework, including SIEM/SOAR platforms and other detection tools.
Leading and Mentoring: Lead complex data science projects from conception to deployment, advocating for engineering best practices and mentoring other data scientists and engineers on the team.
Communicating Actionable Insights: Translate complex data findings and model predictions into clear reports, visualizations, and presentations for technical and non-technical stakeholders to support informed decision-making.
Driving Innovation: Research and apply cutting-edge machine learning techniques to enhance our security posture and stay ahead of emerging cyber threats.
Other
Bachelor’s degree or four or more years of work experience.
Six or more years of relevant experience required, demonstrated through one or a combination of work and/or military experience, or specialized training.
Strong statistical modeling and hypothesis testing skills.
Relevant industry certifications (e.g., CISSP, CDPSE, GMLE).
Experience in a Security Operations Center (SOC) environment.
