SAU Data Scientist

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Note: Applications will be accepted until 11:59 PM on the Posting End Date.

Job End Date

At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness, brings rich diversity to UBC as a workplace, and creates the necessary conditions for a rewarding career.

This position is responsible for the development of machine learning based tools for fisheries catch analysis, redesigning existing data-processing workflows, and building data visualization tools and interfaces for the database and website.

This position supports the in its continued effort to provide robust and reliable fisheries datasets for the public through modernizing the computational and analytical methodologies of the system

Organizational Status
The Data Scientist is a member of the core team. This position reports directly to the Research Unit Manager, Dr Maria L.D. Palomares, and as required to the Principal Investigator, Dr. Daniel Pauly.

Work Performed
Statistical Modelling

• Consult closely with the Principal Investigator (PI) and Research Unit Manager to define research objectives, determine study design parameters, and select appropriate statistical methodologies.
• Evaluate and select statistical programs, R/Python packages, and machine-learning frameworks to ensure alignment with research objectives and study questions.
• Develop, implement, and maintain analytical models across multiple concurrent research projects.
• Design and develop standardized protocols for incorporating underrepresented and small-scale fisheries data.
• Translate and interpret statistical results, enabling researchers and stakeholders to make informed, data-driven decisions.
• Compile, validate, and integrate new datasets (subsistence fisheries, gender-disaggregated data, Indigenous fisheries).
• Create validation workflows to test data integrity and document analytical methodologies to ensure transparency and reproducibility across the team.

Machine Learning Model Development

• Collaborate with subject expert and software engineering team to create scalable machine learning based models for fisheries analysis.
• Research and develop scalable end-to-end data science capabilities covering the full lifecycle of model creation. From data extraction and feature engineering to validation, deployment, and monitoring.
• Write, edit, and publish technical reports, working papers, and training materials based on a thorough understanding of subject matter, user needs, and industry best practices.
• Champion data integrity across the project. Educating other team members about best practices for analytical workflows, standardizing data preparation guidelines, and establishing data quality standards.

Project Coordination

• Establish realistic project timelines and sets clear milestones, ensuring all involved parties are aligned and aware of key dates.
• Regularly plan, execute, and report on projects with team and stakeholders, ensuring all tasks are on track and adjusting timelines as necessary.
• Maintain open lines of communication with all team members and stakeholders throughout the project lifecycle, ensuring clarity, addressing concerns, and fostering a collaborative environment.
• Prepare presentations, reports, documentation, and other deliverables as needed.

Consequence of Error/Judgement
This position requires the execution of a considerable amount of judgement, responsibility and initiative in determining work procedures, and methods. It also requires the coordination of work flows between the database team and domain experts. Incorrect decisions or judgment will directly affect the research group’s…