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.

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: September 19, 2026 (repost)

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.
- Solid 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

📌 Senior Data Science Analyst (Canada)
🏢 University Health Network
📍 Canada