Senior Data Scientist

Cohesity•Santa Clara, CA

10d•Hybrid

About The Position

Interested candidates based outside of the designated areas are welcome to apply, provided they have the indefinite right to work in the job location. Cohesity is a leader in AI-powered data security and management. Aided by an extensive ecosystem of partners, Cohesity makes it easy to secure, protect, manage, and get value from data — across the data center, edge, and cloud. Cohesity helps organizations defend against cybersecurity threats with comprehensive data security and management capabilities, including immutable backup snapshots, AI-based threat detection, monitoring for malicious behavior, and rapid recovery at scale. We’ve been named a Leader by multiple analyst firms and have been globally recognized for Innovation, Product Strength, and Simplicity in Design. Join us on our mission to shape the future of our industry. WANT TO HELP US SIMPLIFY THE WORLD OF DATA MANAGEMENT? Cohesity is seeking a highly skilled and motivated Data Scientist with experience in Security & Data Protection to join our growing team. In this role, you will bridge the gap between academic research and industrial application, developing and deploying advanced ML models to enhance our data protection capabilities and provide deeper insights into cybersecurity threats and ransomware behaviors.

Requirements

Advanced Degree: Master’s or PhD in a STEM discipline (Computer Science, Math, Physics, or Engineering) with a focus on Applied ML or Statistics.
Security Domain Knowledge: Practical understanding of file system metadata, encryption entropy, or network security logs.
Statistical Foundation: Deep knowledge of ML algorithms and the ability to handle highly imbalanced security datasets where "threats" are the rare needles in the haystack.
Software Engineering: Proficiency in Python (Scikit-learn, PyTorch, Pandas) and experience building production-grade ML pipelines.
Research & Publication: A track record of publishing research or presenting at technical conferences, combined with the ability to translate those insights into shippable product features.
Big Data Tech: Familiarity with Spark, Snowflake, or Hadoop to process and analyze massive datasets efficiently.
Bias for Action: A "thinking out of the box" mindset with a focus on solving complex problems in a fast-paced industrial environment.

Responsibilities

Ransomware & Anomaly Detection: Research and implement high-precision models (e.g., Gradient Boosting, LSTMs) to detect early indicators of ransomware, such as high-entropy file changes and unauthorized encryption patterns within backup telemetry.
Automated Data Classification: Apply NLP and Pattern Recognition to automatically identify and categorize sensitive data (PII, PHI, PCI) at scale, ensuring compliance across petabytes of enterprise data.
Proactive Threat Hunting: Develop behavioral analytics to identify "low and slow" data exfiltration or compromised account activity by establishing robust baselines of "normal" system behavior.
Production Pipelines: Design and maintain end-to-end ML pipelines on cloud platforms (AWS/Azure/GCP) that turn theoretical models into reliable, real-world security features.
Adversarial Research: Stay abreast of MITRE ATT&CK frameworks and peer-reviewed security research to ensure models remain resilient against modern evasion techniques.
Stakeholder Communication: Translate complex analytical findings into actionable recommendations for both technical engineers and executive leadership.