Applied Data Scientist - Staff Member

About the role

The Applied Data Scientist will work with and support research staff within ADADS and throughout ORNL to develop and apply modern data science/machine learning techniques to a wide variety of subjects including the characterization of nuclear material and nuclear facility operations, as well as more fundamental physics research. The research involves development and testing of novel data analytics processes and specialized methods to improve measurement fidelity and reduce uncertainties in models used for real-world decisions. Results will be documented by publication in high impact papers, journals, conference papers and technical reports. The work will involve collaboration in a team environment performing project work and developing research proposals.

Responsibilities

• Work with and support research staff within ADADS and throughout ORNL to develop and apply modern data science/machine learning techniques to a wide variety of subjects including the characterization of nuclear material and nuclear facility operations, as well as more fundamental physics research.
• Develop and test novel data analytics processes and specialized methods to improve measurement fidelity and reduce uncertainties in models used for real-world decisions.
• Document results by publication in high impact papers, journals, conference papers and technical reports.
• Collaborate in a team environment performing project work and developing research proposals.

Requirements

• A Bachelor’s degree in physics, computer science, mathematics, or a related field.
• A minimum of 5 years of experience, post bachelor’s, demonstrating capabilities and experience in data science or data analytics.

Qualifications

• Basic understanding of the detection of radioactive materials through measurement of ionizing radiation.
• Experience with applications in nuclear non-proliferation and fundamental physics.
• Experience in one or more of the following ML research areas: Neuromorphic computing, Uncertainty quantification, Unsupervised/Semi-supervised learning and data mining techniques (clustering, embeddings, dimensionality reduction, anomaly detection), Physics informed machine learning, Practical experience using ML models: Dataset curation and preprocessing, Applications leveraging off-the-shelf ML models, Fine-tuning pre-trained models for specific tasks, Designing and training models, from scratch, Familiarity with relevant technologies such as: Computer programming languages such as Python, JavaScript, FORTRAN, C, and C++, Machine learning libraries such as Pytorch, TensorFlow, Scikit-Learn, and OpenCV, 3D game development software such as Unity including VR/AR packages.

Skills

• Strong interest in problem solving and applying new skills and methods.
• Excellent human relation and oral and written communication skills.
• Demonstrated ability to work in a team-oriented environment with a broad range of domestic or international collaborators.
• Commitment to stay current with modern data analytics/machine learning trends.

Benefits

• Potential for publication in high impact papers, journals, conference papers and technical reports.
• Opportunities for collaboration in a team environment.
• Flexibility in working hours and remote work options.
• Competitive pay and benefits programs.
• Employee amenities such as on-site fitness, banking, and cafeteria facilities.
• Relocation assistance.