Lead Machine Learning Engineer (Cyber Security, LMTS)

Join our Trust Intelligence Platform organization as a Lead Machine Learning Engineer. In this role, you will architect the data-driven strategy for our threat detection capabilities, lead the evolution of our threat detection, mentor junior scientists and engineers, and operationalize intelligence. You will have a strong business understanding of cybersecurity problems and a proven track record in data science, with at least 2+ years dedicated to the cybersecurity domain.

• Architect the data-driven strategy for threat detection capabilities, translating vague security threats into concrete mathematical problems.
• Lead the evolution of threat detection by introducing advanced probabilistic modeling, graph analytics, and supervised/unsupervised learning.
• Mentor junior scientists and engineers, and build internal tooling, feature stores, and libraries to enhance team efficiency.
• We are looking for a highly motivated, hands-on lead machine learning engineer with a strong business understanding of cybersecurity problems, who acts as a force multiplier security data scientist for our security organization
• Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring
• Solid foundation in feature engineering techniques and the implementation of feature stores
• Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices
• Experience in formulating ML governance policies and ensuring adherence to data security regulations
• High degree of autonomy with the ability to look at a vague business problem and structure a data-driven solution without needing a predefined roadmap
• Deep understanding and application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines
• Hands-on comfort with high-volume logs and proficiency with Spark/Pyspark, Snowflake, Flink and streaming services such as Apache Kafka
• Proven ability to manage scope, timelines, and stakeholder expectations across multiple organizations
• Ability to explain complex statistical concepts to non-technical stakeholders and executive leadership
• Extensive experience (3-5+ years) in data science, with at least 2+ years dedicated to the cybersecurity domain designing, implementing and deploying systems of anomaly detection, clustering, and graph models in production
• Expertise in advanced Natural Language Processing (NLP) methodologies
• Masters or PhD in a quantitative field
• Experience contributing to open-source security data science tools
• Presentations at major security conferences (Black Hat, DEF CON, BSides) or data conferences
• Previous experience in a mentoring role for junior engineers
• Background in offensive security (Penetration Testing/Red Teaming) with an "attacker's mindset."
• Demonstrated experience conducting research or working collaboratively with Machine Learning (ML) research teams
• Track record of publications and/or patents in quantitative disciplines