Software Developer - Computational Science

Job Title: Software Developer - Computational Science
Team: Automation, Data, and Control
Company: X-Bow Systems

Position Overview: as a Software Developer on X-Bow's Automation, Data, and Control team, you will collaborate with multi-disciplinary teams to design and implement advanced analytical, statistical, and predictive models. These models will drive innovation in X-Bow's product offerings across multiple sectors, including the Make, Build, and Fly divisions.

Key Responsibilities:

• Develop, test, and debug software for projects in the Make, Build, and Fly sectors.
• Maintain and enhance existing codebases for analytical, statistical, and predictive models.
• Analyze performance data from various sources, such as print, test, and flight data, and generate reports.
• Provide software engineering support for research and development (R&D) projects.
• Continuously learn and stay up to date on advancements in artificial intelligence (AI), machine learning (ML), and other relevant fields.

Minimum Qualifications:

• Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related technical field.
• Proven experience with data analytics projects, particularly using Python and Jupyter Notebooks.
• Familiarity with various database systems, including relational (e.g., MySQL, PostgreSQL), non-relational (e.g., MongoDB), graph, and time-series databases.
• Experience with version control systems, especially GitLab.
• Experience with compiling and deploying containerized applications (e.g., using Docker, Kubernetes).
• Ability to communicate with non-technical stakeholders to gather requirements and translate them into technical tasks.

Preferred Qualifications:

• Master's degree in a relevant field such as Computer Science, Data Science, Mathematics, Statistics, or Engineering.
• Experience working in reliability engineering domains.
• Expertise in machine learning and advanced analytical techniques.
• Proficiency in Matlab/Octave.
• Familiarity with time-frequency analysis techniques.
• A strong background in physics.