Digital Forensics Analyst

The modern world faces sophisticated and often invisible criminal activity. Traditional methods of crime and terror prevention and investigation are becoming insufficient as technology rapidly advances and is exploited by adversaries. For nearly 30 years, Cognyte's global team has been dedicated to countering these evolving technological and criminal trends with leading investigative analytics software solutions. We are driven by a commitment to positive impact, fostering a culture of meaningful work, strong friendships, a can-do spirit, and deep curiosity.

As a Cognyter, you will play a crucial role in examining encrypted raw network data. Your responsibilities will include extracting TLS and IP-level metadata, identifying patterns and trends within complex data streams, and building analysis pipelines. You will ensure the accuracy of detection methods through well-defined evaluation metrics. This role requires a strong understanding of network protocols, hands-on experience with packet capture analysis, and solid data analysis or machine learning skills. You will gain expertise in the crypto domain and contribute to the development of a Blockchain analytics product.

Required Qualifications:

• Bachelor's degree in Computer Science or Information Technology.
• Prior experience in network traffic forensics or application traffic analysis.
• Deep understanding of network protocols such as Ethernet, IP, TCP, UDP, and DNS.
• Familiarity with TLS internals.
• Experience working with network traffic data to extract meaningful characteristics or patterns from packet streams.
• Proficiency with tools like Wireshark/tshark, network sniffers, and protocol dissectors.
• Familiarity with basic machine learning and statistical concepts.
• Experience with Python or another scripting/automation language.
• Fluent in English.
• Exceptional attention to detail and high standards for data handling.
• High commitment, responsibility, independence, and initiative.
• Flexibility and adaptability to work across multiple technical contexts.
• Ability to thrive in a dynamic environment.

Nice to Have:

• Research-level machine learning expertise.
• Knowledge of AI models.
• Experience creating labeled datasets and generating synthetic captures.
• Experience with NoSQL databases (e.g., MongoDB).
• Experience with CI/CD solutions (e.g., Jenkins, TeamCity, GitLab CI).
• Experience with Linux and Windows operating systems.
• Understanding of the Software Development Lifecycle (SDLC), including design, execution, testing, and bug reporting.
• Previous experience in a SCRUM team.