Basic Qualifications Bachelor’s of Science in a STEM (Science, Technology, Engineering, Mathematics) related field, plus 8 yrs or Master’s degree plus 6 yrs. CLEARANCE REQUIREMENTS: Ability to obtain a Department of Defense Top Secret security clearance is required at time of hire. Applicants selected will be subject to a U.S. Government security investigation and must meet eligibility requirements for access to classified information. 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. Our Commitment to You: A workplace that allows you to take your career to the next level. Access to expansive and diverse datasets to grow your data science portfolio. Applied research oriented work, alongside award winning teammates to develop practical solutions that directly impact the company’s bottom line. Training budget to help you develop your professional skills, attending conferences, obtaining certifications, even a masters degree. Flexibility to fully manage your own schedule including a hybrid work schedule to allow for a blend of in office and remote work. Opportunities & encouragement to patent and/or publish your work. What You’ll Do: 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. Skills You’ll Bring: Proficiency in ML and DL frameworks, such as scikit-learn, TensorFlow, and PyTorch. Excellent leadership skills. Excellent written and verbal communication abilities. Proficiency in software development, with an emphasis on Python. Understanding of validation methods for ML models. Experience in distributed version control systems and DevOps tools, such as GitLab, Git, Docker, and Kubernetes. Experience with the Linux command line, including bash and shell scripting. Expertise in handling large, complex datasets including image, cyber, NLP, and signal data. Skills in data preparation and corpus filtering using databases like PostgreSQL and MySQL. Familiarity with experimental design for data science projects. Competence & experience in full life cycle ML operations (MLOps). #LI-Hybrid Target salary range: USD $152,367.40/Yr. – USD $169,032.60/Yr. This estimate represents the typical salary range for this position based on experience and other factors (geographic location, etc.). Actual pay may vary. This job posting will remain open until the position is filled. Company Overview At General Dynamics Mission Systems, we rise to the challenge each day to ensure the safety of those that lead, serve, and protect the world we live in. We do this by making the world’s most advanced defense platforms even smarter. Our engineers redefine what’s possible and our manufacturing team brings it to life, building the brains behind the brawn on submarines, ships, combat vehicles, aircraft, satellites, and other advanced systems. We pride ourselves in being a great place to work with this shared sense of purpose, committed to a diverse and exciting employee experience that drives innovation and creates a community where all feel welcome and a part of something amazing. We offer highly competitive benefits and a flexible work environment where contributions are recognized and rewarded. To see more about our benefits, visit General Dynamics is an Equal Opportunity/Affirmative Action Employer that is committed to hiring a diverse and talented workforce. EOE/Disability/Veteran #J-18808-Ljbffr
GitLab PostgreSQL scikit-learn Artificial intelligence (AI) data-management shell Technology savvy Linux optimization Communication Data preparation MLOps deep-learning Docker Information technology (IT) Machine Learning supervised-learning Engineering MySQL hardware Software Developer Git Natural language processing (NLP) Data Analyst mathematics Data Science Python data-visualization STEM DevOps unsupervised-learning experimental-design Kubernetes Analytics TensorFlow Bash Leadership PyTorch