What you will do:

• Lead the research and development of new models and analytic products
• Explore promising areas of future research at the intersection of cybersecurity and machine learning
• Identify data sources; iterate and grow datasets over time
• Explore and analyze data; perform ad-hoc analyses to answer targeted questions
• Partner with subject matter experts to inject their in-depth knowledge into the model creation process
• Iteratively develop models and analytics; move from proof of concept to minimum viable product quickly and efficiently
• Work closely with the engineering team to develop scalable data pipelines, deploy models/analytics, and enact telemetry-driven model improvements over time
• Effectively communicate research results to stakeholders and the research community through documentation, white papers, peer-reviewed publications, and presentations

Qualifications

Minimum Requirements:

• MS/PhD in Computer Science or a related technical field is required.
• Minimum of 5+ years of experience developing and deploying machine learning models in production settings
• Minimum of 3 years experience working on cybersecurity problems, including threat intelligence, network security, endpoint security, and/or cloud security technologies.
• Experience applying a variety of unsupervised, semi-supervised, and supervised machine learning techniques, and the ability to turn big data into actionable intelligence
• Ability to analyze, retrain, and improve machine learning models
• Appreciation for the challenges in applying machine learning in a non-stationary and adversarial environment
• Significant development experience in Python
• Ability to document and explain technical details clearly and concisely (peer-reviewed publications or presentations preferred)
• Ability to provide and receive scientific critiques and work towards data-driven solutions

Desired Qualifications: 

• Experience with scikit-learn, pandas, numpy, or similar packages
• Experience with deep learning packages, including Tensorflow, PyTorch, or Keras
• Practical background with modern ML and NLP approaches, including transfer learning, attention mechanisms, large language models, and embedding methods.
• Familiarity with cybersecurity concepts and a desire to learn from subject matter experts in the cybersecurity and threat intelligence space