PhD Data Scientist for National Security & Defense

Johns Hopkins Applied Physics Lab

Laurel (MD)

On-site

USD 105,000 - 245,000

Full time

8 days ago

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Job summary

The Johns Hopkins University Applied Physics Laboratory (APL) in Laurel, MD invites PhD graduates in statistics, data science, physics, mathematics, or computer science to join a diverse team tackling data-driven defense analytics for national security applications.

You will collaborate with engineers, software developers, statisticians, data scientists, and analysts to develop data collection, processing, and quantitative evaluation for systems used by the Navy and Air Force.

Qualifications

Have a PhD in Data Science, Statistics, Physics, Mathematics, Computer Science or a related field.
Demonstrate strong interpersonal skills and the ability to work independently and on a team.
Have strong foundations in statistical inference, probability, statistical modeling, and experimental design.
Have experience using scientific programming tools such as Python, R, or similar languages for quantitative analysis.
Have experience investigating and adapting modern statistical and computational methods to address emerging analysis challenges in sparse, noisy, or high-dimensional data setting.
Demonstrate experience with statistical modeling, inference, or experimental design approaches such as Bayesian methods, regression, causal inference, or hypothesis testing.
Are able to obtain Interim Secret level security clearance by your start date and can ultimately obtain Top Secret level clearance.

Responsibilities

Work with multi-disciplinary teams to support development of data collection, processing, and analysis efforts to assess the performance of a number of systems supporting the Navy and Air Force.
Develop and evaluate statistical models for complex defense applications, including uncertainty quantification, inference, forecasting, and decision support.
Apply statistical reasoning for data-driven studies, selecting appropriate methods (e.g., Bayesian or frequentist approaches) based on the problem.
Quantify and communicate uncertainty, assumptions, and limitations to support sound decision-making in complex, real-world setting.
Use internal funding opportunities to shape the direction of future research.
Communicate technical knowledge by articulating ideas clearly through papers and presentations to technical staff, management, and government decision makers.