SAU Data Scientist

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

• 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 Sea Around Us 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.

• 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.

• 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.

• →Demonstrated experience working with large biological databases, specifically in fisheries.
• →Proficient in Python, R, SQL, and MS Excel to work with large datasets (over 1 billion rows).
• →Familiar with packages such as Pandas, scikit-learn, and PyTorch for data manipulation and building machine learning models.
• →Knowledgeable in AWS cloud computing environments (S3, EC2, RDS).
• →Experienced in the analysis and interpretation of fisheries-related data, identifying key information, determining implications, providing recommendations and effectively resolving issues.
• →Ability to provide effective and appropriate guidance and counsel (e.g., providing data or instructions on how to obtain data through various methods to external researchers and advising on appropriate uses and interpretation of Sea Around Us data for their purposes).
• →Experienced in writing clean, modular, and well-documented code following Data Science best practices.
• →Effective oral and written communication and interpersonal skills.
• →High level of initiative, curiosity, and willingness to experiment and innovate.
• →Attention to detail and judgment in all work.
• →Ability to give and receive feedback productively.
• →Flexibility and ability to seek creative ideas to solve problems and identify new opportunities.
• →Ability to prioritize and work effectively under pressure to meet deadlines and ensure multiple research projects within the research lab receive timely assistance.
• →Ability to work effectively both independently and in close collaboration with a diverse range of partners and colleagues across campus.