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Cybersecurity Threat Hunter (Data Scientist)

Remote role Full-time Open position

General Summary This role is responsible for developing and maturing analytical models on cybersecurity threat data and processes. They collaborate with product teams to deliver insights to stakeholders, drive analysis of threats, and help improve decisions. This position will drive deployment of a variety of models and workflows using model orchestration and data virtualization solutions. It will require close partnerships with other internal teams. Principal Duties and Responsibilities Possesses the cybersecurity understanding of a Threat Hunter:

  • Understands the data
  • Can build models based on the knowledge of the data

Required skills

  • Data collection and processing
  • Exploratory data analysis
  • Threat Hunt Analysis in a SIEM (Splunk experience required)
  • Knowledge of databases, data structures, and data architecture
  • Strong attention to detail.
  • Ability to work in a team environment
  • Self-starter, requiring limited direction and supervision.
  • Possesses and understanding of statistics and analysis

Preferred skills

  • Model development and evaluation
  • Understanding of LLMs and AI architectures
  • Skilled in artificial intelligence and machine learning.
  • Ability to code in multiple languages.
  • Experience with data visualization tools.
  • Experience with data transformation using common programming tools.
  • Experience applying statistical analysis to large data sets.
  • Able to deliver complex visualizations of data

Job Qualifications/Educational Requirements:

  • Bachelor’s Degree six (6) years’ experience in cybersecurity threat hunting with specialization in complex data analysis. Four additional years of related experience may substitute for degree. experience in mathematical modeling including comprehension of theory, modeling/identification strategies and limitations/pitfalls.
  • Experience with Machine Learning or Artificial Intelligence.
  • SANS certification strongly preferred

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