Senior Data Science Analyst

UHN Research; Toronto; In loco; CAD 79.000 - 118.000; Tempo pieno

Vantaggi offerti da questo lavoro

Competitive offer packages

HOOPP (Healthcare of Ontario Pension)

Transit access and UHN shuttle

Flexible work environment

Development opportunities

Descrizione del lavoro

UHN Research in Toronto is seeking a Data Scientist to join the Structural Genomics Consortium (SGC). You will develop and deploy state-of-the-art ML models that extract actionable insights from DNA-Encoded Library data to identify drug candidates for challenging protein targets.

You will collaborate with structural biologists, medicinal chemists, and bioinformaticians to build end-to-end ML pipelines, apply predictive models, and advance open science and pre-competitive drug discovery in a

Competenze

• Bachelor’s, Master’s or higher in Computer Science, Data Science, Bioinformatics, 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.

Mansioni

• 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.

Conoscenze

Python

ML pipelines

DEL data

MLOps

TensorFlow

PyTorch

Graph neural networks

Transformers

Data visualization

Statistical modelling

Formazione

Bachelor's degree in Computer Science/ Data Science/ Bioinformatics

Master's degree in Computer Science/ Data Science/ Bioinformatics

Strumenti

Python libraries (scikit-learn, pandas, NumPy)

ML frameworks (TensorFlow, PyTorch, Keras)

MLflow

Airflow

DEL data handling

Descrizione del lavoro

• Compensation: CAD 78,954 - CAD 118,424 - yearly

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.

Union:Non-Union
Number of Vacancies:1
New or Replacement Position:New
Site:MaRS
Department:Research
Reports to:Senior Scientist
Salary Range: $78,954- $118,424Per 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

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.

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.