Senior Analyst, Fund Data Science and Modeling

Overview

Why you’ll love working here: high-performance, people-focused culture; commitment to equity, diversity, and inclusion; learning and development initiatives including workshops and Linked In Learning; competitive, company-paid extended health and dental benefits for permanent employees; supportive pension plan; and opportunities to make a difference in Ontario healthcare workforce retirement security.

Reporting to the Director, Data Science & Modeling, Total Fund Analytics, the Senior Analyst will bring deep expertise in applied AI and LLM/RAG-enabled analytics to advance investment reporting, enhance analytical insight generation, and enable governed natural-language access to investment data. The role will design, build, and scale AI-enabled reporting capabilities on top of governed data foundations, semantic metric layers, analytical data models, and curated investment datasets.

This role will also be responsible for improving data warehouse structures, data marts, curated aggregation layers, semantic metric layers, and modern data engineering practices across the reporting ecosystem.

• Total Fund Data Science & Modeling:
  Design, build, and maintain reliable ETL/ELT ingestion and transformation pipelines across data systems such as SAP HANA, Snowflake, Microsoft Fabric, and other enterprise data platforms, with production discipline to support governed reporting and AI-enabled consumption.
• Design, develop, and maintain analytical and semantic data models, including dimensional structures, curated aggregation layers, metric views, and reusable datasets for key investment metrics across reporting, analytics, and AI interfaces.
• Extend the semantic metric layer used by Power BI, Qlik, natural language interfaces, and other tools, ensuring LLM/RAG solutions retrieve accurate and governed definitions, versioned metrics, and roll-ups with streamlined business logic.
• Analyze, model, and curate complex data from multiple workflows and systems to produce trusted key metrics, insights, and recommendations for Senior Management and the Board.
• Embed data quality, lineage documentation, reconciliation controls, metric definitions, and model documentation into pipeline and curated-layer design to support trusted investment reporting.
• Apply AI-assisted development of statistical analysis, machine learning, and traditional data science techniques to accelerate analytical prototyping, identify investment drivers, create simulations, and enhance investment reporting insight.
• Collaborate with Finance, investment, technology, and data stakeholders to identify business requirements, natural language analytics opportunities, and data-centric solutions for reporting and decision support.
• Design and build consolidated investment analytics capabilities, including curated data products, analytical marts, semantic models, and cube-like structures that explain investment results in relation to market conditions, trading strategies, asset mix, and portfolio exposures.
• Develop an understanding of Total Fund Analytics operations, investment reporting processes, institutional investment products, and evolving AI practices.
• Identify high-value natural language query use cases and translate them into prototype and production features grounded in governed datasets and approved metric definitions.
• Design and implement LLM interfaces and Retrieval-Augmented Generation (RAG) pipelines on curated, governed datasets and semantic metric layers for natural language retrieval, explanation, and analysis of trusted metrics and insights.
• Innovation & Operational Efficiency: support leadership in innovation, process improvement, and engagement with external vendors; foster a culture of innovation, experimentation, and continuous learning while ensuring AI solutions remain governed, explainable, secure, and connected to real use cases.
• Stay current with LLM, RAG, machine learning, prompt engineering, and AI practices; evaluate practical applications to institutional investment reporting and analytics; lead research to identify new technologies and methodologies to improve AI-enabled analytics and reporting.