Shape the Defense Strategy: You will own the decision-making process—translating vague security threats into concrete mathematical problems
By championing a rapid prototyping culture, you will validate hypotheses in days rather than months
Detect the "Unknown Unknowns": You will lead the evolution of our threat detection, introducing more advanced probabilistic modeling, graph analytics, supervised and unsupervised learning
Your work will expose sophisticated threats—such as active system intrusions, lateral movement, beaconing, and insider attacks—that evade traditional defenses
Elevate the Organization: You will act as a force multiplier, mentoring junior scientists and engineers
Operationalize Intelligence: By prioritizing engineering rigor (CI/CD, scalable code) and adversarial resilience, you will deliver production-grade models that the SOC actually trusts

Requirements

Extensive experience (3-5+ years) in data science
At least 2+ years dedicated to the cybersecurity domain designing, implementing and deploying systems of anomaly detection, clustering, and graph models in production
Extended practical knowledge and familiarity with security frameworks such as MITRE ATT&CK and OCSF
Hands-on comfort with high-volume logs and proficiency with Spark/Pyspark, Snowflake, Flink and streaming services such as Apache Kafka
Deep understanding and application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines
Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices
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
Experience in formulating ML governance policies and ensuring adherence to data security regulations
Ability to explain complex statistical concepts to non-technical stakeholders and executive leadership
Proven ability to manage scope, timelines, and stakeholder expectations across multiple organizations
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
A related technical degree is required.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

data scienceanomaly detectionclusteringgraph modelsprobabilistic modelingsupervised learningunsupervised learningfeature engineeringMLOpsPython

mentoringcommunicationproblem-solvingstakeholder managementautonomyexplanation of complex conceptsorganizational skillsleadership