Senior Data Scientist Date Posted:

1 October 2026

Closing Date:

October 31, 2026

Recruiter:

Johns Hopkins Applied Physics Laboratory (APL)

Location:

Laurel, Maryland Salary:

USD145,000

to

USD195,000

Job Type & Industry:

Data, Ai & Technology > Ai & Technology Governance, Data, Ai & Technology >

Data Science Contract Type:

Permanent Job Reference:

2997287417-2 Apply for this job now Job Description Senior Data Scientist - Defense & Aerospace Johns Hopkins Applied Physics Laboratory (APL) seeks a Senior Data Scientist to develop advanced analytics and AI solutions for national security and space missions. You will design and deploy machine learning and statistical models, lead end-to-end data science projects, and collaborate with domain experts to deliver decision-quality insights. In APL's mission-driven, research-focused culture, you'll explore cutting-edge methods, publish and prototype, mentor junior staff, and help shape ethical, high-impact AI systems that address critical national and global challenges. Responsibilities Design, implement, and validate advanced machine learning and statistical models for defense and aerospace applications Lead end-to-end data science projects from problem framing through deployment and transition to sponsors Collaborate with engineers, analysts, and mission experts to translate complex operational needs into analytic solutions Mentor and guide junior data scientists, promoting best practices in coding, modeling, and documentation Prototype, evaluate, and communicate novel AI approaches through reports, briefings, and, when appropriate, publications Ensure models are robust, explainable, and aligned with ethical and responsible AI principles Work with software and systems engineers to operationalize models in real-world environments Engage with sponsors to define requirements, present results, and shape future research directions Required Skills Machine learning (supervised, unsupervised, and deep learning) Statistical modeling and inference Python programming (Num Py, pandas, scikit-learn, Py Torch/Tensor Flow) Data engineering and ETL for analytics MLOps and model deployment (CI/CD, containers, cloud) Bayesian methods and probabilistic modeling Experiment design and A/B testing Natural language processing or computer vision (preferred) Big data tools (Spark, distributed computing) Domain knowledge in defense, aerospace, or national security analytics