About the Role
We are seeking a motivated Cybersecurity Data Science Intern with a strong
foundation in data science, machine learning, and a demonstrated interest in
cybersecurity. This role bridges the gap between advanced data-driven techniques and
practical security operations. You will work alongside experienced cybersecurity
professionals to analyze security telemetry, develop models for threat detection, and
apply modern data science methods to real-world security problems.
Responsibilities
Support the development of data-driven models to improve detection of threats,
anomalies, and insider risks.
Apply machine learning techniques such as gradient descent, logistic
regression, clustering, and neural networks to cybersecurity use cases.
Perform exploratory data analysis (EDA) on diverse security datasets (endpoint,
network, cloud, identity, vulnerability).
Collaborate with the security operations and threat intelligence teams to design
metrics, features, and models that enhance detection and response.
Contribute to automation workflows, anomaly scoring models, and visualization
dashboards for SOC analysts.
Research and experiment with AI/ML techniques (e.G., embeddings, NLP,
anomaly detection) to extract actionable insights from large volumes of security
data.
Present findings and proof-of-concept results to both technical and non-technical
stakeholders.
Required Qualifications
Currently pursuing a degree (Bachelor s, Master s, or PhD) in Data Science,
Computer Science, Cybersecurity, Applied Math, or related field.
Strong understanding of machine learning fundamentals (gradient descent,
optimization, supervised/unsupervised learning).
Experience with Python and data science libraries (NumPy, Pandas, scikit-learn,
PyTorch or TensorFlow).
Basic knowledge of cybersecurity concepts such as threats, vulnerabilities,
intrusion detection, and MITRE Telecommunication&CK.
Ability to analyze large datasets and communicate findings clearly.