Research Engineer - Data Science
The Advanced Analytics Team applies expertise in machine learning, large-scale computational architectures, data visualization, and simulation to solve hard research challenges in high impact areas including cybersecurity, biological systems, and distributed sensor platforms. We’ve built automated malware detection systems, deep learning models that can semantically label source code, prototypes to visualize graphs with millions of nodes, and a distributed framework that can process and store sensor network streams on the scale of 100 MB/s.
Our team values creativity, initiative, collaboration, and diversity. We strive for a fun and collegial atmosphere that encourages intellectual crosspollination and professional growth. In short, we’re passionate about empowering our customer’s missions and enjoy working together at the leading edge of technology!
As a Research Engineer, you will apply state-of-the-art machine learning approaches, including deep learning, to new and challenging applications. You will develop data mining techniques to generate datasets and build scalable prototypes with real-world applicability. You may also design interactive visualizations to understand and analyze these datasets and convey model outputs. You will work on multidisciplinary project teams, which include experts from cybersecurity, embedded systems, computing infrastructure, and physics.
- Experience applying machine learning, data mining, natural language processing, data visualization, or similar techniques to challenging problems
- Programming experience with Python, C/C++, Java, or a comparable language
- Ability to work collaboratively on multidisciplinary teams
- Comfortable using statistical analysis, data visualization, or mathematical modeling to illuminate complex problems and communicate research results
- BS degree in Computer Science, Electrical Engineering, Applied Math, or related technical field, or equivalent practical experience.
Nice to Have:
- Familiarity with software development tools and platforms (Git, Linux, etc.)
- Familiarity with data science tools (Keras, Sklearn, Spark, D3, etc.)
- MS degree or equivalent practical experience
- Demonstrations of prior research (blog posts, GitHub repos, publications, etc.)