Senior Manager, Total Fund Data Science and Modeling

Why you’ll love working here:

• high-performance, people-focused culture

• our commitment that equity, diversity, and inclusion are fundamental to our work environment and business success, which helps employees feel valued and empowered to be their authentic selves

• learning and development initiatives, including workshops, Speaker Series events and access to LinkedIn Learning, that support employees’ career growth

• membership in HOOPP’s world class defined benefit pension plan, which can serve as an important part of your retirement security

• competitive, 100% company-paid extended health and dental benefits for permanent employees, including coverage supporting our team's diversity and mental health (e.g., gender affirmation, fertility and drug treatment, psychological support benefits of $2,500 per year, parental leave top-up, and a health spending account).

• optional post-retirement health and dental benefits subsidized at 50%

• yoga classes, meditation workshops, nutritional consultations, and wellness seminars

• the opportunity to make a difference and help take care of those who care for us, by providing a financially secure retirement for Ontario healthcare workers

Job Summary

Reporting to the Director, Data Science & Modeling, Total Fund Analytics, the Senior Manager will lead the design, development, and scaling of advanced data science, AI-enabled analytics, semantic data modeling, and governed investment reporting capabilities across the Total Fund Analytics ecosystem.

The role requires hands-on work on applied AI, LLM/RAG-enabled analytics, governed semantic metric layers, curated investment datasets, data marts, and modern data engineering practices, while expanding the scope to senior leadership, cross-functional influence, delivery oversight, and strategic execution. The Senior Manager will act as a trusted subject-matter leader, translating complex investment data, AI capabilities, metric definitions, and analytical models into clear insights and recommendations that support Senior Management and Board-level oversight.

This is an individual-contributor role with no direct people-management accountability. The Senior Manager will provide indirect leadership by coaching junior staff, developing technical and business skills, sharing knowledge, guiding prioritization and strengthening of data quality and model governance practices, while collaborating with Finance, Investments, Technology, Data, and other cross-functional partners to advance HOOPP’s Total Fund analytics, reporting automation, and AI-enabled insight generation capabilities.

What You Will Do

Total Fund Data Science, Modeling and AI-Enabled Analytics

• Research and lead the implementation of Total Fund data science, modeling, semantic data, and AI-enabled analytics workstreams, ensuring effective planning, coordination, integrated delivery, strong controls, high-quality outcomes, and alignment with Total Fund Analytics and divisional priorities.

• Lead the development and evolution of analytical and semantic data models, including dimensional structures, curated aggregation layers, metric views, reusable datasets, and governed data products that make key investment metrics consistently retrievable across reporting, analytics, dashboards, and AI interfaces.

• Support the Director by providing senior technical oversight for the design, build, and maintenance of reliable ETL/ELT ingestion and transformation pipelines across enterprise data platforms such as SAP HANA, Snowflake, Microsoft Fabric, and other data environments, ensuring production discipline for governed reporting and AI-enabled consumption.

• Guide extension of semantic metric layers used by Power BI, Qlik, natural language interfaces, and other tools, ensuring that LLM/RAG solutions retrieve accurate, versioned, and governed metric definitions, business logic, and roll-ups.

• Review and challenge data models, metric definitions, AI outputs, reporting logic, and analytical results to strengthen quality, consistency, control effectiveness, transparency, and adoption of best practices.

• Embed data quality, lineage documentation, reconciliation controls, metric definitions, model documentation, and governance practices into pipeline, semantic model, and curated-layer design to support trusted investment reporting.

• Apply AI-assisted development, 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 and Strategic Delivery

• Lead and coordinate multiple concurrent research, development, and analytics engineering initiatives across Finance, Investments, Technology, Data, and other stakeholders, ensuring alignment to business priorities and timely delivery of practical outcomes.

• Partner with business and technical stakeholders to identify high-value business requirements, natural-language analytics opportunities, and data-centric solutions that solve practical reporting, oversight, and decision-support problems.

• Lead the design and build 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, portfolio exposures, and performance outcomes.

• Set direction for data science and modeling initiatives by establishing parameters, delegating accountability, guiding prioritization, managing dependencies, and ensuring solutions are scalable, maintainable, and aligned with governed data foundations.

• Oversee the design and implementation of LLM interfaces and Retrieval-Augmented Generation 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.

• Anticipate and manage upstream and downstream dependencies across complex delivery environments, proactively resolving issues related to data quality, model interpretation, controls, business logic, stakeholder expectations, and production adoption.

• Develop and maintain deep knowledge of Total Fund Analytics operations, investment reporting processes, institutional investment products, and emerging AI, data science, analytics engineering, and semantic modeling practices.

Innovation, Governance and Operational Efficiency

• Drive and lead innovation, business process improvement, and the practical adoption of emerging technologies, including artificial intelligence, robotic process automation, machine learning, LLMs, RAG, prompt engineering, and AI-assisted development.

• Foster a culture of innovation, experimentation, continuous learning, and responsible AI adoption while ensuring AI-enabled solutions remain governed, explainable, secure, and connected to real investment reporting use cases.

• Lead research into new technologies, techniques, and methodologies that can improve AI-enabled analytics, semantic retrieval, reporting automation, data quality, investment insight generation, and operating efficiency.

• Drive effective change and issue management by identifying process inefficiencies, recommending practical solutions, and exercising sound judgment on when to resolve, escalate, challenge, or seek alignment on complex matters.

• Build trusted relationships with senior stakeholders across Finance, Investments, Technology, Data, and other cross-functional teams, influencing outcomes beyond the immediate span of control.

• Synthesize complex analysis, AI solution design, data model logic, metric definitions, and analytical trade-offs into clear, concise, and compelling messages for technical and non-technical audiences, including senior leaders.

Technical Leadership, Coaching and Skills Development

• Provide practical guidance to colleagues through peer coaching, technical review, and knowledge sharing on prioritization, stakeholder communication, model documentation, data quality expectations, AI governance, and business adoption.

• Model behaviors that contribute to an inclusive, high-trust, collaborative team environment that encourages curiosity, experimentation, accountability, and disciplined delivery.

• Strengthen team capability, succession readiness, and leadership bench strength by building technical depth in AI, data science, data engineering, semantic modeling, investment analytics, and governed reporting practices.

What You Will Bring

• At least 8-10 years of progressive experience in applied AI, data science, analytics engineering, data engineering, or investment analytics, with demonstrated ability to lead practical business or reporting use cases involving LLMs, RAG, prompt engineering, AI-assisted development, semantic models, or governed metric layers.

• Advanced Python and SQL skills, with the ability to guide AI-assisted coding practices, prototype analytical solutions, apply data science techniques, perform complex transformations, optimize queries, validate results, and review technical outputs for accuracy and maintainability.

• Strong 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, leading, or consuming semantic layers, governed metric views, curated reporting datasets, dimensional models, aggregation layers, analytical marts, or reusable data products 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, prompt engineering, Natural Language Processing, or AI-enabled analytics to business, finance, reporting, or decision-support use cases.

• Broad and deep institutional knowledge, with the ability to make sound recommendations grounded in experience, research, analysis, data validation, control awareness, and professional judgment.

• Strong analytical, quantitative, and problem-solving skills, with the ability to generate holistic insights, connect detailed analysis to broader risks and opportunities, and develop practical solutions that balance AI capability, business value, governance, and maintainability.

• Effective communication skills, with the ability to explain AI solution design, data models, metric definitions, analytical results, trade-offs, and recommendations to technical teams, business partners, and senior stakeholders.

• Demonstrated ability to lead multiple priorities with sound judgment, manage ambiguity, resolve issues, and know when to escalate, challenge assumptions, or seek alignment on complex matters.

• Proven ability to build strong working relationships across all levels of the organization in a team-oriented, collaborative environment.

• A Master’s degree or higher degree in Computer Science, Statistics, Data Science, Finance, Engineering, or a related field.

• High attention to detail, accuracy, completeness, and documentation quality.

• Commitment to HOOPP’s core values of professionalism, accountability, collaboration, compassion, and trustworthiness.

HOOPP may use artificial intelligence tools to assist in screening, assessing and selecting applicants for this position. These tools support our recruitment process but do not replace human judgment and decision-making.