EDUCATION REQUIREMENTS:

Bachelor’s of Science in a STEM (Science, Technology, Engineering, Mathematics) related field, plus 8 yrs or Master’s degree plus 6 yrs.
Due to the nature of work performed within our facilities, U.S. citizenship is required.
Responsibilities for this Position:
The Deep Learning Analytics Center of Excellence (DLACoE) at General Dynamics Mission Systems is actively seeking a seasoned data scientist with a robust background in Deep Learning (DL), advanced data visualization, software development, and full Machine Learning (ML) life cycle management. This individual serves in a pivotal role that interfaces directly with our business development teams, leading the identification and cultivation of innovative AI/ML initiatives. Furthermore, the role entails the responsibility of engaging with our clientele, educating them about what AI can and cannot do, our approach to problem solving, and our progress to date on their programs.
In addition to these client-facing duties, this person plays a critical internal team role. They lead our interdisciplinary teams in tackling complex DL challenges by leveraging and refining cutting-edge training methodologies and model architectures to solve problems across a diverse range of data modalities underwater, on land, in the air, and in space. The role also encompasses a mentorship dimension, guiding and fostering the professional growth of fellow team members within the DLACoE.
Candidates for this position are expected to demonstrate a high level of technical acumen, excellent communication skills, and a passion for continuous learning within the ever-evolving field of artificial intelligence.

• Leading teams of data scientists on full life cycle data-driven solutions.
• Mentoring fellow data scientists and providing guidance on their efforts.
• Data wrangling, preprocessing, and manipulation for ML applications.
• Applying advanced statistical methods and reporting techniques to large datasets.
• Interpreting data analysis and experimental results, and communicating insights.
• Developing ML systems for both supervised and unsupervised learning tasks.
• Mastering DL algorithms and addressing their limitations related to hardware or data.
• Conducting experiments to enhance model performance and clearly reporting outcomes to both technical and non-technical audiences.
• Optimizing DL architectures for performance and supporting systematic optimization efforts.
• Deploying ML solutions across diverse platforms, from large clusters to small SWaP (Size, Weight, and Power) environments.
• Monitoring ongoing model performance to detect and adjust for data drift.
• Following best practices for software documentation and reporting