Job Summary: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.
What you will do: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 sufficient 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 that make key investment metrics consistently retrievable across reporting, analytics, and AI interfaces.
- Extend the semantic metric layer used by Power BI, Qlik, natural language interfaces, and other tools, ensuring that 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 that support Senior Management and the Board to fulfill its oversight and governance role.
- 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 result drivers, create simulations, and enhance investment reporting insight.
Research & Development
- Collaborate with Finance, investment, technology, and data stakeholders to identify business requirements, natural language analytics opportunities, and data-centric solutions that solve practical reporting and decision-support problems.
- Participate in the design and building of 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 and maintain a deep understanding of Total Fund Analytics internal operations, investment reporting processes, institutional investment products, and emerging AI, data science, and analytics engineering practices.
- Partner with the related stakeholders to 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 top of curated, governed datasets and semantic metric layers to enable natural language retrieval, explanation, and analysis of trusted business metrics and insights.
Innovation & Operational Efficiency
- Support the Director in leading innovation, business process improvement, sparking an innovation mindset within the Department, and engaging external vendors to showcase innovation opportunities to the Department (such as artificial intelligence, robotic process automation, machine learning, etc.).
- Foster a culture of innovation, experimentation, and continuous learning while ensuring AI-enabled solutions remain governed, explainable, secure, and connected to real investment reporting use cases.
- Stay current with LLM, RAG, machine learning, prompt engineering, and AI-assisted development practices, and evaluate their practical application to institutional investment reporting and analytics.
- Lead research and identify new technologies, techniques, and methodologies that can improve AI-enabled analytics, semantic retrieval, reporting automation, data quality, and investment insight generation.
What you bring:- 5+ years of experience in applied AI, data science, analytics engineering, data engineering, or senior analytics roles, with demonstrated ability to apply LLMs, RAG, prompt engineering, or AI-assisted development to practical business or reporting use cases.
- Advanced Python and SQL skills, including the ability to use AI-assisted coding practices to prototype analytical solutions, apply traditional data science techniques, perform complex transformations, optimize queries, and validate results.
- Solid foundational knowledge of institutional investment products and analytics, including public and private markets, derivatives, benchmarks, and performance metrics in an institutional investment setting.
- Experience designing or consuming semantic layers, governed metric views, curated reporting datasets, dimensional models, aggregation layers, or analytical marts used by BI, analytics, and AI-enabled retrieval tools.
- Familiarity with cloud technologies, production-grade ingestion and orchestration patterns, and hands-on experience working with both cloud and on-premises databases. Experience with Snowflake, Microsoft Fabric, SAP HANA, Power BI, or Qlik is especially desirable.
- Practical experience applying generative AI, LLMs, Retrieval-Augmented Generation (RAG), prompt engineering, Natural Language Processing (NLP), or AI-enabled analytics to business, finance, reporting, or decision-support use cases.
- Strong problem-solving skills and the ability to think critically, creatively, and pragmatically to develop innovative solutions that balance AI capability, business value, governance, and maintainability.
- Effective communication skills to explain AI solution design, data models, metric definitions, analytical results, and trade-offs, and translate them into business recommendations for technical and non-technical stakeholders.
- A Bachelor's or Master's degree in Computer Science, Statistics, Data Science, Finance, Engineering, or a related field.
- Able to work independently with minimal direction, complement existing team strengths, and take ownership of AI-enabled analytics outcomes from prototype through practical adoption.
- High attention to detail, accuracy, and completeness.
- Proven ability to interact confidently and effectively with all levels of the organization and to build strong working relationships in a team-oriented, collaborative environment.
- Share HOOPP's core values of professionalism, accountability, collaboration, compassion, and trustworthiness.
The expected annual base salary range for this role is: $103,000 - $153,000 CAD
The actual base salary offered to the successful candidate may vary based on multiple factors including, but not limited to, individual's expertise and level of experience applicable to the role they are being offered. This role is eligible to participate in discretionary incentive plan(s), subject to the terms and conditions of the applicable incentive plan text. This job is for an existing vacancy.
