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University Health Network

Senior Data Science Analyst

College Street, Toronto, ON, Canada

Contract

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Applications open in a new tab.

Job details

Job type: Contract
Apply by: Oct 30, 2026
Working hours: 37.5 hours per week
Positions available: 1
Posting language: English
Posted: Oct 8, 2026

Original job description

Company Description

UHN is Canada’s #1 hospital and the world’s #1 publicly funded hospital. With 10 sites and more than 44,000 TeamUHN members, UHN consists of Toronto General Hospital, Toronto Western Hospital, Princess Margaret Cancer Centre, Toronto Rehabilitation Institute, The Michener Institute of Education and West Park Healthcare Centre. As Canada's top research hospital, the scope of biomedical research and complexity of cases at UHN have made it a national and international source for discovery, education and patient care. UHN has the largest hospital-based research program in Canada, with major research in neurosciences, cardiology, transplantation, oncology, surgical innovation, infectious diseases, genomic medicine and rehabilitation medicine. UHN is a research hospital affiliated with the University of Toronto.

UHN’s vision is to build A Healthier World and it’s only because of the talented and dedicated people who work here that we are continually bringing that vision closer to reality.

www.uhn.ca

Job Description

Union: Non-Union
Number of Vacancies: 1
New or Replacement Position: New
Site: MaRS
Department: Research
Reports to: Senior Scientist
Salary Range: $78,954 - $118,424 Per Year
Hours: 37.5 hours per week
Shifts: Day Shifts
Status: Permanent Full-time
Closing Date: October 30, 2026

Position Summary:
We seek a talented and driven Data Scientist passionate about accelerating drug discovery through machine learning and AI to join our team at the Structural Genomics Consortium (SGC). As a Data Scientist at SGC, you will play a central role in developing and deploying state-of-the-art machine learning models that extract actionable insights from complex biomedical datasets, including DNA-Encoded Library (DEL) screens, to identify and optimize drug candidates for challenging protein targets. Your work will directly contribute to our mission of open science and pre-competitive drug discovery.

The candidate will be assisted by software developers to leverage internal tools (AIRCHECK) in order to develop, train and test machine learning (ML) and artificial intelligence (AI) predictive models, independently or in collaboration with other experts in the Haibe-Kains and Schapira labs and other SGC collaborators.

Duties

Design, build, and deploy end-to-end machine learning pipelines, from raw data ingestion and preprocessing through model training, evaluation, and production deployment.
Develop and apply predictive models to DEL screening data and other high-dimensional biomedical datasets to prioritize drug-like compounds for defined protein targets.
Implement and fine-tune deep learning architectures (transformers, graph neural networks, generative models) for molecular property prediction and de novo drug design.
Perform rigorous exploratory data analysis, statistical modelling, and visualization to surface meaningful patterns in large, noisy biomedical datasets.
Establish and maintain MLOps best practices, including model versioning, pipeline automation, experiment tracking, and reproducible workflows.
Collaborate closely with structural biologists, medicinal chemists, and bioinformaticians to translate scientific questions into robust machine learning solutions.
Present findings, methodologies, and model performance clearly to both technical collaborators and non-technical stakeholders.
Stay current with the latest advances in ML/AI for drug discovery and proactively evaluate and integrate novel approaches into the team’s research workflows.

Qualifications

Bachelor’s, Master’s or higher degree in Computer Science, Data Science, Bioinformatics, Computational Chemistry, or a related field.
Masters’ with 4 years related experience.
Bachelor’s degree with 6 years related experience
Proven experience designing and implementing machine learning models, particularly in drug discovery or biomedical domains.
Strong expertise in developing end-to-end ML pipelines, from data preprocessing to model deployment, using Python and related libraries (e.g., scikit-learn, pandas, NumPy).
Hands-on experience working with DNA-Encoded Library (DEL) data or similar complex, high-dimensional biomedical datasets.
Familiarity with MLOps practices, including model versioning, pipeline automation, and reproducible workflows (e.g., MLflow, Airflow).
Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or Keras, with experience building transformers, graph neural networks, or generative models.
Deep understanding of feature extraction, model evaluation metrics, and optimization techniques for noisy biomedical data.
Demonstrated ability to perform exploratory data analysis, statistical modelling, and data visualization for large datasets.
Experience collaborating within multidisciplinary teams, effectively bridging domain experts and technical staff.
Strong written and verbal communication skills, with the ability to present ML findings to both technical and non-technical audiences.
Proven track record of staying current with emerging ML techniques and the ability to innovate on new model architectures or approaches in drug discovery.

Additional Information

Why join UHN?

In addition to working alongside some of the most talented and inspiring healthcare professionals in the world, UHN offers a wide range of benefits, programs and perks. It is the comprehensiveness of these offerings that makes it a differentiating factor, allowing you to find value where it matters most to you, now and throughout your career at UHN.

Competitive offer packages
Government organization and a member of the Healthcare of Ontario Pension Plan (HOOPP https://hoopp.com/)
Close access to Transit and UHN shuttle service
A flexible work environment
Opportunities for development and promotions within a large organization
Additional perks (multiple corporate discounts including: travel, restaurants, parking, phone plans, auto insurance discounts, on-site gyms, etc.)

Current UHN employees must have successfully completed their probationary period, have a good employee record along with satisfactory attendance in accordance with UHN's attendance management program, to be eligible for consideration.

All applications must be submitted before the posting close date.

UHN uses email to communicate with selected candidates. Please ensure you check your email regularly. At University Health Network (UHN), artificial intelligence technologies may be used to assist in the screening, assessment, and selection of candidates for this position.

Please be advised that a Criminal Record Check may be required of the successful candidate. Should it be determined that any information provided by a candidate be misleading, inaccurate or incorrect, UHN reserves the right to discontinue with the consideration of their application.

UHN is an equal opportunity employer committed to an inclusive recruitment process and workplace. Requests for accommodation can be made at any stage of the recruitment process. Applicants need to make their requirements known.

We thank all applicants for their interest, however, only those selected for further consideration will be contacted.

Department: Research

Compensation: CAD 78954 - CAD 118424 - yearly

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More about this job

Responsibilities

Design, build, and deploy end-to-end machine learning pipelines to extract actionable insights from complex biomedical datasets. Collaborate with multidisciplinary teams to apply predictive models for drug candidate identification and optimization.

Requirements

Requires a Bachelor's degree with 6 years of experience or a Master's degree with 4 years of experience in a relevant field. Must possess strong expertise in Python, machine learning frameworks, and MLOps practices within a biomedical or drug discovery context.

Skills

Machine learning
Artificial intelligence
Python
Deep learning
Data science
Bioinformatics
Computational chemistry
Drug discovery
MLOps
TensorFlow
PyTorch
Keras
Statistical modelling
Data visualization
Graph neural networks
Transformers

Benefits

Healthcare of Ontario Pension Plan
Transit access
UHN shuttle service
Flexible work environment
Corporate discounts
On-site gyms

Visa sponsorship

Not detected in the job text

Keywords

Machine learning
Artificial intelligence
Drug discovery
DNA-Encoded Library
Bioinformatics
Computational chemistry
Python
Scikit-learn
Pandas
NumPy
MLOps
MLflow
Airflow
TensorFlow
PyTorch
Keras
Transformers
Graph neural networks
Generative models
Statistical modelling
Data visualization
Biomedical research
Structural biology
Medicinal chemistry
Predictive modeling
Feature extraction
Open science
Research
Healthcare
Data analysis

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