Data Scientist

at  CANADIAN NUCLEAR LABORATORIES CNL

DESCRIPTION

The Data Scientist will provide AI/Machine learning expertise and data analytics support for research and development projects across multiple areas, such as nuclear safety and analysis, material research, nuclear forensics and environmental monitoring. They will be expected to analyze data sets from different sources in order to support ongoing work activities; to collaborate across disciplines with experimentalists, physicists and engineers; to develop appropriate experimental design approaches to ensure the collection of high-quality data; to develop, design, implement, and evaluate machine-learning algorithms; to perform sensitivity and uncertainty analysis; to write high-quality and well-documented code, research reports and papers; and to assist in meeting the anticipated needs for the expansion at CNL in data analytics and machine learning applications.

QUALIFICATIONS

• Bachelor’s (Honours) degree in Engineering or Science from a university of recognized standings, areas of preference are Machine Learning, Computer Science, Applied Statistics or related field from a university of recognized standing.
• Strong applied experience is essential (i.e. documented applied analysis from research projects, co-op terms, data science competitions, graduate or employment work). This includes intermediate or advanced programing skills in Python and machine learning libraries, such as TensorFlow, scikit-learn, PyTorch, etc. with experience analysing real-world datasets.
• Ability to work in a team environment with colleagues with different technical expertise as well as perform tasks independently.
• Willingness to learn new skills and demonstrate innovative approaches to problem solving.
• Strong verbal and written communication skills. The candidate is expected to report on progress through internal reports and to publish the results in peer reviewed journals and present at scientific/engineering conferences.
• Familiarity with another programming language (such R, MATLAB, C++), with version control (Git, SVN), and with high performance computing in UNIX/Linux environments is considered an asset.
• Experience in designing, implementing, and optimizing NLP algorithms for various applications is considered an asset.
  Required Security Clearance Eligibility: Reliability Status with Site Access Security Clearance (SASC), which has a minimum requirement of 3 years of verifiable history in Canada, Australia, New Zealand, United States and/or the United Kingdom. CNL implements security screening in accordance with the Treasury Board of Canada Secretariat “Standard on Security Screening” and the “Policy on Government Security”.
  Why CNL?
  Does working with a team across Canada to advance nuclear science and technology for a clean and secure world speak to you? We’re reinventing ourselves to be the pace setters, so we can lead the charge in solving the problems that matter, like building the next generation of clean nuclear and hydrogen energy solutions, developing new and better-targeted cancer treatments, and continuing to lead the world in environmental remediation.

• Contribute to the application and development of data analytics methods across diverse areas. Specific examples include statistical/machine learning methods for anomaly detection in nuclear data, evaluation of nuclear safety margins and their uncertainties, and time-series forecasting.
• Applies appropriate methodology when implementing studies, including processing data and performing statistical analysis, machine learning and/or computational modelling using software programs such as Python or R. Takes responsibility for the computational integrity and accuracy of the analysis.
• In collaboration with domain experts, develops new methods and technologies as an outcome of the research performed. Applicant is interested in and capable of independent work, including learning and applying new machine learning methods, and writing and modifying computer programs in software such as R, MATLAB, Python, RAVEN, Dakota, etc.
• Interprets and communicates results. Presentations on research will be delivered internally to CNL, at national and international venues/conferences, and to external customers. Work will be documented in internal CNL reports and published in peer reviewed journals.