CodeHunter · 1 day ago
Data Scientist, Cybersecurity
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Cyber SecurityInformation Technology
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Responsibilities
Apply data science methodologies to classify and analyze different types of malware.
Develop models to understand how malware components may be assembled into campaigns.
Investigate the variance in malware samples within a single family.
Study the evolution of malware families from initial discovery to extinction.
Explore gaps between Endpoint Detection and Response (EDR) capabilities and off-line analysis to improve malware detection and response.
Measure complexity, entropy, and other relevant attributes of malware using data science techniques.
Perform differential analysis between malware samples to identify unique characteristics.
Enhance techniques for detecting packed or obfuscated malware.
Identify patterns and correlations in malware data using machine learning algorithms.
Evaluate the effectiveness of different classification methods in predicting the behaviors that types or families of malware may exhibit.
Determine which malware attributes are valuable for training large language models (LLMs) and machine learning (ML) algorithms.
Qualification
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Required
Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, or relevant industry experience.
Strong foundation in data science and machine learning.
Proficiency in programming languages such as Python, R, or similar and familiarity with frameworks such as numpy, scikit-learn, PyTorch, etc.
Excellent analytical and problem-solving skills.
Eagerness to learn about malware and cybersecurity.
Preferred
Experience with large language models and their application in data analysis.
Familiarity with cybersecurity concepts and static and dynamic analysis tools.
Strong communication skills to convey complex technical concepts to non-technical stakeholders.
Benefits
401K
Health coverage
Vision and dental coverage
Company-sponsored training
Parking or metro benefits
Catered lunches
Generous PTO policy
Company
CodeHunter
At CodeHunter, we have developed a new way to detect malware hidden deep within code.
Funding
Current Stage
Early StageTotal Funding
$5.21M2020-05-14Series Unknown· $5.21M
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