Senior Data Scientist III - Machine Learning for Undersea Acoustics

Pacific Northwest National Laboratory•Richland, WA

1d•Onsite

About The Position

Pacific Northwest National Laboratory (PNNL) is seeking a Senior Data Scientist III in the AI and Data Analytics Division of its National Security Directorate. The role focuses on developing and evaluating deep learning methods for undersea acoustic data, supporting national security and naval sensing missions. The position is based in Richland, Washington, or Seattle, Washington, and requires onsite presence. The successful candidate will lead research projects or major technical efforts, developing analytical methods from initial questions through evaluation and research prototypes. Work may address detection, classification, localization, or characterization of underwater sources using deep learning, Fourier analysis, signal processing, mathematical analysis, and experiments. Related opportunities include time-series analysis, representation learning, anomaly detection, uncertainty quantification, multimodal sensing, and evaluation of agentic AI systems that integrate deep learning with acoustic analysis tools. The role involves collaboration with researchers, domain specialists, mission partners, software engineers, and machine learning engineers. The scientist will help translate user needs into research questions, assess technical barriers to transition and deployment, communicate findings, contribute to proposals, and mentor colleagues. The position requires the ability to obtain and maintain a federal security clearance and U.S. citizenship. Selected candidates are subject to federal background investigation and pre-employment and random drug testing.

Requirements

Bachelor's degree and 5 or more years of data science experience, or master's degree and 3 or more years of data science experience, or Ph.D. and 1 or more years of data science experience.
Experience developing, training, and evaluating deep learning models for acoustic, sonar, audio, radar, seismic, or related sensor data.
Experience implementing, training, and evaluating deep learning models in Python with PyTorch.
Ability to obtain and maintain a federal security clearance.
U.S. citizenship.
Selected candidates must meet federal background-investigation eligibility requirements for access to classified matter.
Selected candidates must meet the stated drug-testing requirements, including demonstrating 12 consecutive months of non-use of illegal drugs before completing the requisite national security questionnaire.

Nice To Haves

Experience developing and evaluating deep learning methods for undersea acoustic detection, classification, localization, or related problems.
Experience applying Fourier analysis or other signal-processing methods to acoustic or related sensor data.
Experience with time-frequency representations or array-processing methods for acoustic analysis.
Familiarity with agentic AI systems and methods for evaluating their performance and reliability.
Familiarity with national security applications of deep learning and undersea acoustics, and with submitting proposals.

Responsibilities

Lead development, training, and evaluation of deep learning methods for undersea acoustic detection, classification, localization, and related problems using Fourier analysis or signal processing, mathematical analysis, and experiments.
Lead research projects or major technical efforts, coordinate contributors, and plan work, resources, and milestones.
Evaluate methods against baselines using representative data, assessing detection and classification performance, false alarms, robustness to propagation and environmental variability, uncertainty, and computational demands.
Build and validate research prototypes and evaluate agentic AI systems with software and machine learning engineers, addressing technical barriers to transition and deployment.
Work with mission partners and domain specialists to translate user needs into research questions, and communicate findings through analyses, technical reports, and discussions.
Develop research ideas and collaborate with project leaders on proposals.
Mentor colleagues, help recruit and onboard researchers, and share methods and findings across disciplines.
Follow applicable quality, safety, and security requirements.