Johns Hopkins Applied Physics Laboratory (APL) seeks a Data Scientist to apply advanced analytics, machine learning, and statistical modeling to national security, space, and health challenges. In this role, you will design and implement data-driven solutions, build scalable data pipelines, and collaborate with multidisciplinary experts to turn complex data into mission impact. You will explore cutting-edge techniques, communicate insights to diverse stakeholders, and contribute to research, publications, and prototype systems in a highly collaborative, mission-driven environment that supports continuous learning and innovation. Design and implement machine learning and statistical models to solve complex national security, space, and health problems. Collect, clean, integrate, and analyze large, heterogeneous datasets from multiple sources. Develop production-quality data pipelines, analytical tools, and reusable model components. Collaborate with scientists, engineers, and domain experts to translate mission needs into analytical approaches. Visualize and communicate findings, methods, and assumptions to technical and non-technical stakeholders. Document methodologies, experiments, and results; contribute to technical reports and publications. Evaluate and compare modeling approaches; perform rigorous testing, validation, and performance tuning. Stay current with emerging data science methods, tools, and best practices and apply them to APL missions. Machine learning (supervised and unsupervised methods) Statistical analysis and modeling Python programming (NumPy, pandas, scikit-learn, etc.) Deep learning frameworks (TensorFlow or PyTorch) Data wrangling and ETL pipeline development SQL and NoSQL databases Data visualization (e.g., Matplotlib, Plotly, Tableau) Cloud platforms (AWS, Azure, or GCP) MLOps and model deployment practices Version control and collaborative development (Git) Medical Insurance Dental Insurance Vision Insurance Life Insurance 401k Relevant technical certifications (e.g., AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure Data Scientist Associate, Google Professional Data Engineer) Public Trust or security clearance eligibility, if required by project Demonstrated publication, conference presentations, or open-source contributions in data science or machine learning
