Deep Learning Researcher, Fundamental Forecasting

CPP Investments | Investissements RPC•Toronto, ON

21d•Onsite

About The Position

The Active Equities department at CPP Investments aims to deliver alpha in global public equity markets through company-specific fundamental research. The Fundamental Forecasting team specifically focuses on creating a diversifying alpha stream by forecasting financial outcomes at scale and systematically monetizing these forecasts. This involves developing large-scale forecasts of individual company fundamentals using alternative data and advanced predictive techniques, and then designing, implementing, and managing portfolios based on these insights. The role is for a deep learning researcher with strong quantitative foundations to be a high-impact individual contributor within the Fundamental Forecasting team. This researcher will own and drive the deep learning research roadmap, reporting directly to the team’s Managing Director. Key responsibilities include leading the design, training, and evaluation of transformer-based models to forecast company fundamentals and financial KPIs at scale, utilizing large and diverse data sources. The position emphasizes applying state-of-the-art deep learning techniques to enhance the quality, stability, and economic value of fundamental forecasts, and translating these improvements into actionable investment signals. It is suited for an experienced deep learning researcher eager to apply modern transformer-based models to real-world financial forecasting and investing problems, aiming to deliver measurable impact on alpha generation.

Requirements

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.
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.
Demonstrated interest in financial markets and willingness to develop domain expertise are essential.
Candidates must exemplify CPP Investments’ guiding principles of high performance, integrity, and partnership.
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.

Nice To Haves

Familiarity with distributed data processing frameworks (e.g., PySpark) is an asset.
Experience in quantitative investing, portfolio optimization, or equity research is an asset.

Responsibilities

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.