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About the Role

Reporting to the Director, Data Science & Modeling, Total Fund Analytics, this Senior Manager role leads the design, development, and scaling of advanced data science, AI-enabled analytics, semantic data modeling, and governed investment reporting capabilities. The role involves hands-on work with applied AI, LLM/RAG-enabled analytics, and modern data engineering practices, while also focusing on senior leadership influence, delivery oversight, and strategic execution. This individual contributor role provides indirect leadership through coaching and knowledge sharing, collaborating with cross-functional partners to advance analytics, reporting automation, and AI-enabled insight generation.

Responsibilities

Research and lead the implementation of Total Fund data science, modeling, semantic data, and AI-enabled analytics workstreams.
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.
Provide senior technical oversight for the design, build, and maintenance of reliable ETL/ELT ingestion and transformation pipelines.
Guide extension of semantic metric layers used by Power BI, Qlik, natural language interfaces, and other tools.
Review and challenge data models, metric definitions, AI outputs, reporting logic, and analytical results.
Embed data quality, lineage documentation, reconciliation controls, metric definitions, model documentation, and governance practices into design.
Apply AI-assisted development, statistical analysis, machine learning, and traditional data science techniques.
Lead and coordinate multiple concurrent research, development, and analytics engineering initiatives.
Partner with business and technical stakeholders to identify high-value business requirements and data-centric solutions.
Lead the design and build of consolidated investment analytics capabilities.
Set direction for data science and modeling initiatives by establishing parameters, delegating accountability, and guiding prioritization.
Oversee the design and implementation of LLM interfaces and Retrieval-Augmented Generation pipelines.
Anticipate and manage upstream and downstream dependencies across complex delivery environments.
Develop and maintain deep knowledge of Total Fund Analytics operations, investment reporting processes, and emerging AI/data science practices.
Drive and lead innovation, business process improvement, and the practical adoption of emerging technologies.
Foster a culture of innovation, experimentation, continuous learning, and responsible AI adoption.
Lead research into new technologies, techniques, and methodologies.
Drive effective change and issue management by identifying process inefficiencies and recommending practical solutions.
Build trusted relationships with senior stakeholders across various teams.
Synthesize complex analysis, AI solution design, data model logic, and analytical trade-offs into clear messages for diverse audiences.
Provide practical guidance to colleagues through peer coaching, technical review, and knowledge sharing.
Model behaviors that contribute to an inclusive, high-trust, collaborative team environment.
Strengthen team capability, succession readiness, and leadership bench strength.

Requirements

At least 8-10 years of progressive experience in applied AI, data science, analytics engineering, data engineering, or investment analytics.
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.
Ability to guide AI-assisted coding practices, prototype analytical solutions, apply data science techniques, perform complex transformations, optimize queries, and validate results.
Strong foundational knowledge of institutional investment products and analytics.
Experience designing, leading, or consuming semantic layers, governed metric views, curated reporting datasets, dimensional models, aggregation layers, analytical marts, or reusable data products.
Familiarity with cloud technologies, production-grade ingestion and orchestration patterns, and hands-on experience working with both cloud and on-premises databases.
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.
Ability to generate holistic insights, connect detailed analysis to broader risks and opportunities, and develop practical solutions.
Effective communication skills, with the ability to explain complex topics to technical and non-technical audiences.
Demonstrated ability to lead multiple priorities with sound judgment, manage ambiguity, resolve issues, and know when to escalate, challenge assumptions, or seek alignment.
Proven ability to build strong working relationships across all levels of the organization in a team-oriented, collaborative environment.
High attention to detail, accuracy, completeness, and documentation quality.
Commitment to HOOPP’s core values of professionalism, accountability, collaboration, compassion, and trustworthiness.

Skills

Applied AI
Data Science
Analytics Engineering
Data Engineering
Investment Analytics
LLMs
RAG
Prompt Engineering
AI-assisted development
Semantic models
Governed metric layers
Python
SQL
Institutional investment products and analytics
Cloud technologies
ETL/ELT
Snowflake
Microsoft Fabric
SAP HANA
Power BI
Qlik
Generative AI
Natural Language Processing
Natural Language Interfaces
Data quality
Model governance
Change management
Issue management
Stakeholder management
Communication
Collaboration

Experience Level

Senior Manager
8-10 years of progressive experience

Education Level

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

Salary/Compensations

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.

Benefits

High-performance, people-focused culture
Commitment to equity, diversity, and inclusion
Learning and development initiatives, including workshops, Speaker Series events and access to LinkedIn Learning
Membership in HOOPP’s world class defined benefit pension plan
Competitive, 100% company-paid extended health and dental benefits for permanent employees
Coverage supporting 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

About the Company

HOOPP is committed to making a difference and helping take care of those who care for us, by providing a financially secure retirement for Ontario healthcare workers.

Equal Opportunity

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.