Data Scientist Intern

MATT by Mattersec Labs is an ExploitOps platform focused on continuous security validation for modern software environments. We help teams identify exploitable weaknesses, prioritize what matters, and verify that fixes hold as their software evolves.

We're building at the intersection of AI, cybersecurity, and software engineering. Joining Mattersec means working closely with the founders and engineers on practical problems that shape how organizations secure increasingly AI-built software.

We're looking for a Data Scientist Intern to help build and evaluate data-driven capabilities within MATT. You'll work with engineering and security teams to analyze security data, design experiments, and develop approaches that improve detection, prioritization, and validation.

This is a hands-on opportunity to apply statistics, machine learning, and LLM evaluation to real product challenges, with guidance from the team and ownership of defined projects.

Key responsibilities include:

• Collect, clean, and structure datasets from security findings, code analysis, and agent workflows.
• Explore data to identify patterns, investigate false positives, and surface opportunities to improve the product.
• Develop and test statistical and machine learning approaches for classification, prioritization, and risk assessment.
• Help build evaluation datasets and benchmarks to measure the accuracy, reliability, and consistency of AI models and agents.
• Support reproducible data processing and experimentation pipelines.
• Communicate findings through clear visualizations, documentation, and recommendations.
• Collaborate with engineers to test promising approaches and measure their performance on real-world data.

• Currently pursuing or recently completed a bachelor's or master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. Candidates with strong relevant projects are also welcome.
• A solid foundation in statistics, probability, and core machine learning concepts.
• Working knowledge of Python and libraries such as pandas, NumPy, and scikit-learn.
• Familiarity with SQL, data cleaning, exploratory analysis, and data visualization.
• Understanding of model evaluation, including precision, recall, overfitting, and data leakage.
• Ability to approach open-ended problems methodically and explain findings clearly.
• Comfort working independently on defined tasks and collaborating in a remote team.

Nice to have:

• Exposure to LLMs, AI agents, evaluation frameworks, or benchmark design.
• Interest in cybersecurity, secure software development, or security analytics.
• Familiarity with Git and reproducible experimentation.
• Academic, personal, or open-source projects demonstrating practical data science skills.

Prior professional experience in cybersecurity is not required. We value curiosity, sound reasoning, and a willingness to learn.

• Practical experience applying data science to AI security problems.
• Close collaboration with founders, security researchers, and engineers.
• Ownership of scoped projects with measurable product impact.
• Experience taking ideas from initial analysis through experimentation and product evaluation.

• Apply with your resume and links to relevant projects, GitHub repositories, or a portfolio. Include a brief note about one project: the problem you tackled, how you evaluated your approach, and what you learned.