The Senior Data Analytical Specialist/Scientist is fully allocated to HealthMap. Working with minimal direction and leading others, the role analyzes the ODBM and methodology and builds and validates it within PHO. The role aligns with and advances the Public Health Data Utility (PHDU) as the platform foundation and operates within the Analytics Engine operating model.

Key Responsibilities

• Analyze the DLSPH chronic-disease-burden model and methodology to support a build within PHO systems.
• Build, validate, and run the model within PHOs environment, aligned to and advancing the PHDU (with an R/Azure bridge until PHDU is available).
• Support data acquisition through data-sharing agreements and governance processes where required for the ODBM to run within PHO systems.
• Document methods and transfer knowledge to PHO staff so the ODBM can be sustained beyond the engagement.
• Participate in related HealthMap documentation, development, testing, end-user training, and knowledge transfer; work with functional experts, data architects, ETL developers, and PHDU technical teams to deliver sustainable solutions.

Key Deliverables

• Documented methodology, code, data-acquisition and, as available, analytical and reporting requirements.
• DLSPH model built, validated, and running within PHO systems (PHDU-aligned).
• Knowledge transfer to PHO staff to sustain the capability.

General Skills (Required)

• Excellent analytical, problem-solving, and decision-making skills.
• Strong consulting and relationship-management skills to work with client groups, technical teams, data stewards, data engineers and stakeholders to confirm requirements, options, constraints,
  
  
  
  and delivery decisions.
• Ability to communicate complex quantitative methods, assumptions, outputs, and limitations clearly to technical and non-technical audiences.
• Senior-level experience with statistical methods and models, including their underlying data requirements, model validation, and reproducible analytical workflows.
• Ability to manipulate and analyze complex data from structured and unstructured sources to support evidence-based decision‑making.
• Experience designing data‑ingestion, transformation, quality‑checking, and reproducible data‑processing workflows.
• Broad understanding of data management, business analysis, database architecture, governance, and information visualization.
• Proficiency in R and/or Python, query languages, and version‑controlled analytical code to ensure that any ODBM analytical workflows can be implemented, run, troubleshooted, and documented.
• Robust investigative, mathematical, and statistical skills to assess model logic, test assumptions, resolve data or code issues, and support sustainable knowledge transfer.

Requirements

Assets / Desirable

• Experience with R and PowerBI.
• Experience with population or public-health data and chronic-disease, microsimulation,
  
  
  
  or burden-of-disease modelling.
• Experience building on, or adapting, an external academic or research model within a production environment.
• Familiarity with cloud analytics environments (e.g., Azure) and platform-based delivery products such as Databricks.
• Knowledge and understanding of the Accessibility for Ontarians with Disabilities Act (AODA) and related standards.

Experience Level — Senior

• Be an advanced professional able to apply concepts, practices, and procedures in practice.
• Work with minimal direction and lead and train others in technical components and concepts.
• Plan, lead, and deliver complex deliverables that provide options for decisions within the organization.
• Bring a high level of expertise in the required skill set, specialized in the technical area, and provide specific advisory support as required.

Must Haves

• Excellent analytical, problem-solving, and decision-making skills.
• Strong consulting and relationship-management skills to work with client groups, technical teams, data stewards, data engineers and stakeholders to confirm requirements, options, constraints, and delivery decisions.
• Ability to communicate complex quantitative methods, assumptions, outputs, and limitations clearly to technical and non-technical audiences.
• Senior-level experience with statistical methods and models, including their underlying data requirements, model validation, and reproducible analytical workflows.
• Experience designing data‑ingestion, transformation, quality‑checking, and reproducible data‑processing workflows.