Staff - Non Union
M&P - AAPS
AAPS Salaried - Statistical Analysis, Level B
Data Scientist
Amed Laboratory | Department of Paediatrics | Faculty of Medicine
$7,622.83 - $11,886.67 CAD Monthly
The Compensation Range is the span between the minimum and maximum base salary for a position. The midpoint of the range is approximately halfway between the minimum and the maximum and represents an employee that possesses full job knowledge, qualifications and experience for the position. In the normal course, employees will be hired, transferred or promoted between the minimum and midpoint of the salary range for a job.
November 21, 2026
Note:Applications will be accepted until 11:59 PM on the Posting End Date.
Job End Date
November 22, 2026
This position is expected to be filled by promotion/reassignment and is included here to inform you of its vacancy at the University.
This position is subject to the satisfactory completion of required background checks.
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.
The UBC Department of Pediatrics, Division of Endocrinology, located at BC Children’s Hospital invites applications for a Machine Learning Engineer to join a multidisciplinary translational research program focusing on building a digital health technology, called the Intelligent Care Platform (ICP). ICP aims to bring together disparate sources of data (clinical, wearable, patient-reported outcome/experience measures) to generate insights through Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), that can be translated into actionable insights at the point of care and shared with healthcare providers and patients to enable personalized and preventative care. This project will also evaluate ICP’s impact on advancing the Quintuple Aim (enhancing patient experience, improving population health, mitigating rising healthcare costs, reducing clinician burnout, and striving for health equity) by deploying the platform in a quasi-experimental trial at the BC Children’s Hospital Diabetes Clinic in 200 children and youth living with type 1 diabetes. This project is funded by the CIHR and Breakthrough T1D (formerly JDRF) – $3.5M over 4 years (2023-2027).
We are looking for a Data Scientist to join our research team. We are seeking an individual with experience and interest in Machine Learning (ML). This position requires experience in computing science, software engineering, or a related area, with a focus on machine learning, statistics and/or data science. You will have experience in AI/ML/DL using Python, TensorFlow, PyTorch, etc. Expertise in data science and experience working with clinical data is an asset.
Work performed:
Participate in the research as described above with a specific focus on training, validating and applying AI/ML/DL using the data available in ICP
Oversees the cleaning and maintenance of data from multiple varied sources (e.g. internal, external, administrative health, clinical)
Conducts extensive modelling of complex large data sets using a variety of software and languages to explore data validity, and conduct statistical analysis
Develops approaches and techniques for the solution of problems; communicates aspects of complex problems so that appropriate decisions can be made by the research team
Performs data extraction and linkage from multiple databases for analysis
Develops statistical approach and implements program code (SAS, Python, R, Matlab, etc.)
Maintains and updates syntax, metadata files (e.g., data dictionaries that guide use of variables), documentation procedures and methods
Performs statistical modelling and data analyses, including sophisticated modelling techniques, hypothesis testing, and power and sample size calculations
Interprets, summarizes and documents statistical analysis and results
Integrates data from comprehensive data sources and develop robust data pipelines to enable regular updating of data
Ensures compliance with ethical and data security policies and procedures
Respects privacy and ethics around the use of sensitive and privileged data sets
Work with expert faculty researchers and academics, post-docs, research associates and research assistants
Work with industry partners and vendors, including engineers responsible for building, maintaining and further developing the platform
Aid in the writing of research ethics applications and publications
Work effectively in a team environment with limited supervision
Respect and value diversity in the workplace environment
Consequence of Error/Judgement
The incumbent is given wide latitude for exercising judgment in performing specialized duties and responsibilities. A lack of judgment could harm the project and partner organizations' research and funding.
Supervision Received
The incumbent will be able to work independently with minimal supervision and regularly report to the Principal Investigator. The incumbent will also receive support from research collaborators as needed.
Minimum Qualifications
Post-graduate degree in Statistics. Minimum of three years of related experience in research analysis, or the equivalent combination of education and experience.
Willingness to respect diverse perspectives, including perspectives in conflict with one’s own.
Demonstrates a commitment to enhancing one’s own awareness, knowledge, and skills related to equity, diversity, and inclusion.
Preferred Qualifications
M.Sc. from one of the following fields: computer science, statistics, applied mathematics, data science; or a minimum of two years of experience in research analysis or equivalent education and experience preferred
Excellent programming experience (e.g., Python, R, Java, JavaScript, C#, C++)
Excellent knowledge and experience with relational and non-relational database systems (e.g., SQL, Hive, Cassandra, MongoDB, Spark, Neo4J, Pandas)
Excellent knowledge and hands-on experience with various machine learning packages and models (e.g., PyTorch, Scikit-Learn, TensorFlow)
Excellent communication skills and able to explain complex concepts to non-technical audiences
Excellent organizational skills and able to juggle multiple projects.
