Analyst, Senior Bioinformatics

Responsibilities

• 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.
• 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.
• Why join 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 wi

Benefits

Additional Information

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: June 19, 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.

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