Data Scientist

What Ontario Health offers:

Achieving your career goals is a priority to us. Benefits of working at Ontario Health may include the following based on employment type:

Here is what you will be doing:

The Data Scientist roles plays a pivotal role within Ontario Health's Data and Decision Sciences (DDS) team. The DDS team leverages advanced analytics modelling, including Natural Language Processing (NLP) and machine learning, to derive valuable insights from complex healthcare data, optimize system operations, and support strategic healthcare initiatives.

The ideal candidate will have a proven track record in AI, machine learning, Natural Language Processing, advanced statistical modeling, and analytics. A primary responsibility of this role is to drive AI innovation within the organization by designing, developing, and deploying end-to-end machine learning and NLP solutions for a variety of healthcare applications, utilizing Ontario Health's cloud infrastructure (Azure and AWS). This work will require close collaboration with clinical, digital and IT teams to ensure proper integration of models into healthcare operations. The Data Scientist will independently lead high-impact, mission-critical initiatives aimed at enhancing operations and care delivery within Ontario Health.

Additionally, as a trusted advisor to clinical and business teams, the Data Scientist will work closely with Ontario Health's programs to guide strategic decisions that improve access, quality, sustainability, and patient experience in healthcare. The incumbent will also manage and mentor teams as needed, and effectively communicate with key stakeholders across the organization.

Moreover, the Data Scientist will contribute to DDS projects by providing expertise in machine learning, statistical modeling, and data analysis using both administrative and clinical data. The incumbent will produce analytical products that track trends and forecast clinical and business outcomes to support evidence-based decision-making.

• Lead the design and execution of data analyses and modeling projects, including developing project plans, producing reports, building analytic tools for stakeholders, and creating detailed documentation.

• Design, develop, and implement scalable, end-to-end machine learning and AI models, with a focus on healthcare applications, including but not limited to natural language processing (NLP) models.

• Lead the development of NLP models and pipelines to process, analyze, and code medical reports, ensuring model accuracy and reliability in collaboration with domain experts.

• Implement NLP models for healthcare tasks such as text classification, named entity recognition (NER), and text filtering.

• Leverage cloud platforms, particularly Azure Machine Learning, for the development, deployment, and monitoring of end-to-end machine learning solutions.

• Build and optimize end-to-end machine learning pipelines, encompassing data ingestion, feature engineering, model training, evaluation, and deployment using Azure ML Studio.

• Utilize cloud services (e.g., Azure) to create robust, scalable machine learning pipelines.

• Conduct literature reviews and research cutting-edge NLP models tailored for healthcare use cases.

• Collaborate with DevOps and MLOps teams to implement continuous integration/continuous deployment (CI/CD) workflows for machine learning solutions.

• Apply advanced statistical techniques to develop predictive models and support decision-making across healthcare projects.

• Supervise and mentor staff when needed, providing guidance on complex technical projects and ensuring high-quality output.

• Collaborate with multidisciplinary teams, including digital, IT, and infrastructure teams, to ensure successful deployment of models into production environments.

• Apply a flexible approach to data science, statistical modeling, and analytics techniques to address evolving health system priorities, adapting to projects beyond AI/ML as require

• Drive the expansion of Ontario Health's AI capabilities through collaboration with internal teams and external partners.

• Present findings, model results, and project updates to senior leadership and healthcare stakeholders, effectively using data-driven insights to influence decision-making.

• Document all work, including code, processes, and lessons learned, to facilitate knowledge sharing across the team.

• Elevate Ontario Health's analytics reputation by publishing and presenting projects at external industry and academic conferences.

• Build strong, collaborative relationships with internal and external stakeholders, representing Ontario Health's analytics capabilities in various settings.

Education and Experience

• A master's degree (PhD preferred) in data science, computer science, operations research, applied mathematics, or a related field.

• Minimum 4 years of experience in data science, AI, machine learning, or advanced analytics, with experience leading end-to-end projects preferably healthcare sector.

• Extensive experience linking and analyzing large datasets.

• Proven experience with NLP, including developing models for healthcare text data.

• Hands-on experience with Azure Machine Learning or similar cloud platforms for building and deploying machine learning models.

• Experience leading large, complex analytics initiatives, ideally in healthcare sector.

• Proven ability to translate complex data into tools and actionable recommendations for stakeholders.

• Experience with Ontario's healthcare datasets, including DAD, NACRS, NRS, CCRS, OHIP, clinical registries, and RPDB, is a strong asset.

Knowledge and Skills

• Proficient in Python, R, SQL and similar programming languages for data science.

• Experience with NLP libraries such as SpaCy, Hugging Face, NLTK or similar.

• Knowledge of Statistical modelling, Machine Learning AI concepts, methods, and toolkits (Deep Learning, Natural Language Processing, Large Language Models (LLMs))

• Expertise in machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.

• Understanding of deep learning techniques and NLP models such as transformers (BERT, GPT, etc.).

• Experienced with LLMs (extractive and generative), fine-tuning and LLM model development. Familiar with higher level trends in LLMs and open-source platforms.

• Experience and interest in working with Azure ML Studio, Azure Data Lake, and other Azure services.

• Proficiency in version control (Git) and experience with MLOps practices is a bonus.

• Experience with ML lifecycle management using MLflow, including experiment tracking, packaging, and hyperparameter tuning.

Leadership & Soft Skills:

• Strong leadership capabilities with a demonstrated ability to manage teams, mentor staff, and independently drive project success.

• Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.

• Proven problem-solving abilities and a flexible approach to working on a wide variety of projects.

• Personal drive and a strong ability to self-manage.

• Ability to work independently and collaboratively in a fast-paced, team-oriented environment.

• Excellent oral and written communication skills.