Fortinet’s FortiNDR Cloud is seeking a Staff Data Scientist to conduct research on detecting network threats using cutting edge machine learning techniques. Our behavioral observation development process includes the deconstruction of threat actor capabilities, behaviors, and techniques into observable artifacts. We deal with pretty BIG network data sets (Petabytes in size, trillions in numbers). You will work with other developers and researchers to develop new models and behavioral observations to detect threats. You will be creating and maintaining data pipelines to query, transform and process data. You will be building end to end processes that can make use of machine learning models in threat detection. This is your opportunity to join a top-tier research team within Fortinet to solve difficult problems and make a difference in network security and protect critical assets.

REQUIRED SKILLS:

• Experience: 9+ years of experience in machine learning, data science, or a related field, with at least 3 years in cybersecurity.
• Education: Bachelor’s or Master’s degree in computer science, Data Science, Machine Learning, or other quantitative fields. PhD is a plus.
• Strong proficiency in Python.
• Experience in data analysis with SQL and writing complex SQL queries.
• Knowledge and experience with Production Machine Learning Development Lifecycles from Data Analysis, Training, Validation, to Deployments.
• Experience with either AWS, Azure, or GCP, or similar cloud compute environments
• Hands-on experience with MLOps tools and cloud-native ML solutions, optimizing workflows, and deploying models into production environments.
• Strong written and verbal communication skills.

PREFERRED SKILLS:

• Experience with Snowflake Cloud Data Platform.
• Familiarity with Network analysis frameworks such as Zeek.
• Familiarity with cybersecurity standards, protocols, and compliance requirements.
• ML/Cyber security papers presented at industry specific conferences.
• Advanced certifications in Cybersecurity
  The Canada base salary range for this full-time position is expected to be between $124,000 - $165,000 annually. Wage ranges are based on various factors including the labour market, job type, and job level. Exact salary offers will be determined by factors such as the candidate’s subject knowledge, skill level, qualifications, and experience.
  Fortinet strives to provide you and your family with a comprehensive benefits package. Benefits eligibility starts on your first day of hire and comprises of 100% company paid medical, dental, and vision coverage, including a Health Spending Account and a Personal Spending Account that gives you flexibility to spend where you need it the most. Our Employee & Family Assistance Plan (EFAP) offers you and your family access to various services like counseling, legal advice, mental health resources etc. We also provide critical illness, disability, and life insurance, as well as a Group Registered Retirement Savings Plan (RRSP) with a company match to help you save faster for retirement. We offer competitive Paid Time Off and flexible leave policies, including paid health days, to help you take care of yourself and your family members.
  All roles are eligible to participate in the Fortinet equity program. Bonus eligibility is reviewed at time of hire and annually at the Company’s discretion.
  

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• Risk Modeling and Explainability: Develop probabilistic models and statistical frameworks to detect and respond to network-based threats, integrating data from network logs, user behaviors, and threat intelligence to provide actionable risk assessments.
• Model Development: Design, train, and evaluate machine learning models to detect suspicious network activity.
• Data Pipeline Engineering: Create and optimize data pipelines that process, clean, and transform raw cybersecurity data into formats suitable for machine learning.
• Deployment and Optimization: Implement machine learning models in production environments, focusing on model optimization for high performance and scalability, especially in cloud-based or hybrid environments.
• Research and Prototyping: Stay current on the latest ML techniques and tools; prototype and experiment with new algorithms to continuously enhance our capabilities.
• Threat Analysis Collaboration: Work alongside threat analysts to incorporate domain expertise into model features, ensuring model relevance to real-world cyber threat scenarios.
• Automation and Monitoring: Develop automated tools for model training, evaluation, and monitoring to streamline processes and maintain model performance over time.
• Code Review and Mentorship: Participate in code reviews, provide feedback, and mentor junior engineers to foster best practices in the team.