Purpose. Performance. People.

Joining CPP Investments means joining one of the world’s most admired and respected institutional investors to drive a single mandate: to deliver strong, sustainable returns for generations of Canadians.

With a long-term horizon and global reach, we deploy capital at scale across public and private markets. Our size, stability, and disciplined investment philosophy allow us to pursue complex opportunities and build enduring partnerships worldwide.

For our people, this means meaningful work with tangible impact, real opportunity, and collaboration with exceptional colleagues who value partnership and performance. Here, you’ll contribute to outcomes that matter alongside team members committed to excellence and shared success.

Role Summary:

The mandate of Active Equities is to deliver alpha in the global public equity markets, leveraging CPP Investments’ structural competitive advantages and proprietary sources of edge to generate alpha through company-specific fundamental research. The Fundamental Forecasting team’s objective is to create a diversifying alpha stream by forecasting financial outcomes at scale and systematically monetizing these forecasts. The team develops large-scale forecasts of individual company fundamentals using alternative data and advanced predictive techniques, and designs, implements, and manages portfolios that translate these insights into investment decisions.

We are seeking a deep learning researcher with strong quantitative foundations to play a key role in the Fundamental Forecasting team within the Active Equities department. In this role, you will own and drive the deep learning research roadmap as a high-impact individual contributor, reporting directly to the team’s Managing Director. You will lead the design, training, and evaluation of transformer-based models to forecast company fundamentals and financial KPIs at scale, leveraging large and diverse data sources.

The role focuses on applying state-of-the-art deep learning techniques to improve the quality, stability, and economic value of fundamental forecasts, and translating these improvements into actionable investment signals. This position is ideal for an experienced deep learning researcher who is excited to develop and apply modern transformer-based models to real-world financial forecasting and investing problems, and to deliver measurable impact on alpha generation.

Accountabilities & Qualifications:

Accountabilities

• Lead the development of transformer-based forecasting models and research pipelines, translating investment objectives into robust deep learning architectures.
• Design, train, and evaluate transformer architectures tailored to financial time series, panel data, and alternative data sources.
• Systematically evaluate model performance using rigorous statistical, robustness, and economic metrics, and clearly articulate sources of performance improvements.
• Communicate research findings and trade-offs to the Managing Director and broader Fundamental Forecasting team.
• Implement and integrate models into the production forecasting pipeline in collaboration with engineering and research partners.
• Advance the deep learning research agenda within Fundamental Forecasting by monitoring external research, identifying high-impact opportunities, and prioritizing initiatives based on expected impact.
• Partner with colleagues across Fundamental Forecasting to evaluate the downstream impact of forecasts and support monetization in live portfolios.
• Develop domain expertise in company fundamentals and contribute to broader forecasting and research initiatives within the Portfolio Construction group and Active Equities.

Qualifications

• Advanced degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field.
• Demonstrated experience developing, training, and evaluating deep learning models from scratch is required.
• Strong practical experience designing and training transformer-based models in PyTorch; proficiency in Python and the modern deep learning ecosystem.
• Experience working with large, structured, and unstructured datasets; familiarity with distributed data processing frameworks (e.g., PySpark) is an asset.
• The ideal candidate would have 5+ years of relevant research or applied deep learning experience, with demonstrated ownership of complex modeling initiatives.
• Strong problem-solving skills and comfort working with ambiguous, open-ended research problems.
• Strong experimental rigor, attention to detail, and ability to produce high-quality, reproducible research.
• Ability to manage multiple research initiatives and prioritize effectively in a fast-paced environment.
• Experience in quantitative investing, portfolio optimization, or equity research is an asset; demonstrated interest in financial markets and willingness to develop domain expertise are essential.
• The ideal candidate combines scientific rigor with a strong appreciation for the investment context and thrives in an environment where both research quality and investment relevance are highly valued.
• Candidates must exemplify CPP Investments’ guiding principles of high performance, integrity, and partnership.

You are motivated to contribute to something larger than yourself, approach complex challenges with rigor, and hold yourself to high standards in a collaborative, performance-driven environment.

Inclusion & Accessibility

CPP Investments is committed to equitable access to employment and building a workforce that reflects diverse talent and perspectives. If you require accommodation at any stage of the recruitment process, please let us know and we will work with you to meet your needs.

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CPP Investments is committed to a secure and transparent recruitment process. We will never ask candidates for payment or financial information at any stage of hiring. All legitimate opportunities are posted on our careers page, and communications will come from our applicant tracking system, Workday.

CPP Investments may use AI tools to help screen and assess applicants by analyzing resumes and applications for relevant skills and experience. These tools support, but do not replace, human decision-making.

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