Email Threat Detection Expert
5 days ago
**About Us:**
Trellix, the trusted CISO ally, is redefining the future of cybersecurity with its GenAI-powered platform. Our comprehensive security solutions empower over 53,000 customers to gain confidence in the protection and resilience of their operations.
We're dedicated to addressing the 4-million-person cybersecurity talent gap by creating a home for anyone seeking a meaningful future in cybersecurity. We invite candidates from various industries to join us in shaping cutting-edge security solutions.
**Job Description:**
We are seeking an experienced Data Scientist to join our Email Security team and contribute to the development of innovative data-driven models to detect and mitigate email-borne threats. This role will be instrumental in enhancing our ability to detect sophisticated attacks, improve automation, and proactively safeguard our customers.
Key Responsibilities:
- Design, develop, and implement data-driven models to detect and prevent email-based security threats.
- Analyze large datasets of email communications to identify patterns, anomalies, and emerging threats.
- Leverage behavioral pattern detection techniques to identify suspicious email activities and attack vectors.
- Collaborate with security researchers, engineers, and threat intelligence teams to improve detection capabilities and reduce false positives.
- Monitor, evaluate, and refine models to maintain high detection efficacy and adapt to evolving threats.
- Develop and optimize feature extraction techniques tailored to identifying malicious emails, attachments, and URLs.
- Work closely with software engineering teams to integrate data models into production systems.
- Participate in cross-functional meetings, providing insights based on data analysis findings and contributing to proactive defense strategies.
- Maintain and enhance data pipelines for training, deploying, and monitoring analytical models.
- Stay current with advancements in data science, artificial intelligence, and cybersecurity to continuously improve detection methodologies.
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