Job Title:

Staff Data Scientist

About Trellix:

Trellix is the cybersecurity company transforming security operations with artificial intelligence (AI), analytics, and automation to create a resilient digital world. Our market-leading AI-powered XDR Platform learns and adapts to disrupt active threats and empower CISOs with living security. The platform’s open architecture and broadest set of native security controls across endpoint, email, network, cloud, and data security integrates with over 500 third-party tools to create multi-vector, multi-vendor event correlation and context to speed up investigations. The Trellix Advanced Research Center provides an additional layer of protection by continuously informing the platform on the latest threat actor TTPs (techniques, tactics, and procedures) and recommendations from millions of global sensors. Trellix and an extensive partner ecosystem accelerate technology innovation and empower over 40,000 business and government customers to build confidence in the protection and resilience of their operations. Learn more at Trellix.com.

Role Overview:

The role will work on solving critical cyber security problems using machine learning. This includes identifying data requirements, feature engineering, model training and deployment. Typical areas of exposure would in the field of Malware detection, Malicious URL classification and Threat Hunting.

About the role: 

• As a Staff Data Scientist, you will work in a team of smart data scientist reporting to Data Science Director that does full-lifecycle full-stack Machine Learning product development, from feature engineering to model building and evaluation.

• Our team's use cases include but are not limited to attack detection, threat hunting, malware detection and anomaly detection, and MLOps. These various use cases give you the opportunity and challenges to experience multiple machine learning algorithms and tools.

• You will work on more than one project at any giving point in time. You will mentor junior data scientists in the team.

• You will own at least one project end to end throughout the year. From problem identification, data exploration, feature engineering, model development and deployment.

• This is a full stack data scientist role. You may need to build data processing pipeline, feature extraction pipeline and automated model training and deployment pipelines.

About you:   

• A Degree in Machine Learning, Computer Science, or Electrical Engineering, Mathematics, Statistics

• In-Depth understanding of all major Machine Learning and Deep learning algorithms, supervised and unsupervised both

• Should be an expert in ML problem formulation, feature engineering, model training and deployment

• Passion for leveraging ML/AI to solve real-world business problems

• 8+ years of industry experience in one or more machine/deep learning frameworks

• 8+ years of industry experience with Python/Pyspark/R and SQL

• Experience in solving multiple business problems using Machine Learning

• Experience with various public cloud services (such as AWS, Google, Azure) and ML automation platforms (such as Kubeflow)

• Familiarity with Security Domain will be a plus

• Experience in C++ programming will be a plus

• Having Patent/Publications in the field of ML/AI will be a plus

• Solid computer science foundation

• Good written and verbal communication

Company Benefits and Perks:

We work hard to embrace diversity and inclusion and encourage everyone to bring their authentic selves to work every day. We offer a variety of social programs, flexible work hours and family-friendly benefits to all of our employees.

• Retirement Plans
• Medical, Dental and Vision Coverage
• Paid Time Off
• Paid Parental Leave
• Support for Community Involvement

We're serious about our commitment to diversity which is why we prohibit discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status.