Senior Director, Security Analytics

Salesforce•Bellevue, WA

2d•Hybrid

About The Position

Salesforce is seeking a Senior Director, Security Analytics to own the strategy, development, and operation of data science and machine learning (ML) capabilities built directly into their customer-facing security products. This role is crucial for maintaining real-time, automated threat detection and containment in high-scale SaaS environments, protecting millions of users and hundreds of thousands of customer organizations. The Senior Director will lead a specialized team of security data scientists, ML engineers, and detection engineers, collaborating with cross-functional product and engineering teams to safeguard customer identities, network perimeters, AI products, and counter emerging AI attacks at scale.

Requirements

10+ years of experience in security data science, machine learning engineering, or building customer-facing security analytics products at scale.
Track record of shipping production ML models into commercial software platforms across multi-tenant SaaS environments.
Strong grasp of security-specific data science/ML problems, including imbalanced datasets, cold-start tenant models, adversarial ML evasion, and model explainability.
Proven record of detecting and stopping attackers at cloud-level scale.
Experience working directly with Product, Engineering, and Incident Response teams to drive automated threat containment features to completion.
Ability to align cross-functional senior leadership around complex security data science initiatives.
Excellent written and verbal communication skills, with the ability to communicate complex statistical modeling and AI security concepts clearly to executive stakeholders and customers.

Nice To Haves

Familiarity with real-time big data streaming infrastructure (Spark, Flink, Kafka, Databricks).
Experience with modern ML frameworks (PyTorch, TensorFlow, Scikit-Learn).

Responsibilities

Own the customer-facing security analytics roadmap end to end, driving strategy for AI product defense, identity, network and application programming interface (API) threat prevention, and adversarial AI threat containment.
Build native customer-facing safeguards against AI-specific attacks, including prompt injection, model inversion, data poisoning, and unauthorized large language model (LLM) data exfiltration.
Design analytics that use automated containment mechanisms, such as adaptive step-up authentication, API rate-limiting, and session revocation, to neutralize active threats instantly without disrupting customer operations.
Partner with Product Management, user experience (UX), and executive stakeholders to translate complex statistical outputs into clear security insights and trust dashboards for customer administrators.
Report system performance, model metrics, drift analysis, and threat containment service level agreements (SLAs) to leadership.