Summer Intern – Data Science

At Cynnovative, we leverage machine learning, computer science, and software engineering to address high-impact problems in the cyber domain, specifically those which are critical to U.S. national security. We primarily extend fundamental research to invent, design, develop, and deploy prototype solutions that support persistent problems in this domain.

As a paid intern at Cynnovative, you will join a team applying state-of-the art techniques from machine learning, including deep learning, to protect networks and devices from cyber threats. You will interact with multiple sources of data, including cybersecurity logs, network traffic, packet capture (PCAP), and open source intelligence (OSINT). You should have a familiarity with some of these forms of data, or feel comfortable developing a domain expertise in cybersecurity. 

This is a research and development role; therefore, prospective interns should be comfortable spending a portion of their time in self-directed research, including by reading academic papers, proposing solutions to challenges arising from sparse or messy data, and developing prototypes and proofs of concept of never-before-tried techniques. Interns will also work closely with data scientists and software engineers at Cynnovative in order to support existing research projects, model building, and software development.

At the conclusion of this internship, the candidate will have developed a portfolio of either data science or software engineer work in support of cybersecurity research and development. The intern will work with a supervisor to identify and execute an individual project and write a short white paper summarizing research findings and software developed.

Due to the ongoing COVID-19 pandemic, this position may be primarily remote.

• Implement supervised and unsupervised algorithms for data fusion, predictive analytics, and anomaly detection tasks in cybersecurity
• Implement algorithms for natural language processing, information extraction, and vector space models
• Implement reinforcement learning and other agent-based planning and reasoning techniques
• Implement graph-theoretic algorithms and machine learning models for graphs
• Work with Data Scientists to implement rapid prototypes of analytical techniques found in literature and to write and test code that implements machine learning and deep learning models in both R&D and operational environments
• Perform requirements analysis, system architecture design, code quality reviews, and configuration management controls
• Collaborate with others to author research papers, white papers, proposals, and presentations to customers supporting ongoing business development
• Work closely with Software Engineers to harden and transition prototypes for production environments

• Undergraduate or graduate student in a quantitative field or computer science
• Proficiency in Python or Java
• Excellent written and verbal communication skills
• U.S. Citizenship

• Familiarity with deep learning frameworks (PyTorch, TensorFlow, Keras, etc.)
• Familiarity with distributed computing (Hadoop, Spark, etc.)
• Familiarity productionizing data science workflows or containerized environments (Docker, Flask, SQL, Gvisor, etc.)
• Familiarity with the fundamentals of machine learning concepts
• Familiarity with Cybersecurity data, tools, and techniques