Data Scientist Date Posted:

1 November 2026

Closing Date:

December 1, 2026

Recruiter:

Johns Hopkins Applied Physics Laboratory (APL)

Location:

Laurel, Maryland Salary:

USD145,000

to

USD185,000

Job Type & Industry:

Data, Ai & Technology >

Data Science Contract Type:

Permanent Job Reference:

3045732353-2 Apply for this job now Job Description 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. Responsibilities 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. Required Skills Machine learning (supervised and unsupervised methods) Statistical analysis and modeling Python programming (Num Py, pandas, scikit-learn, etc.) Deep learning frameworks (Tensor Flow or Py Torch) Data wrangling and ETL pipeline development SQL and No SQL 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)