Senior Data Science Analyst

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.