Machine Learning Engineer

Join CPPIB's Investment Science team and lead the transformation of investing through an increased usage of data and advanced analytical techniques such as machine learning. This role is ideal for a top performer passionate about alpha generation and interested in the intersection of finance, computer science, and mathematics. If you're eager to drive impactful investment decisions using empirical research and state-of-the-art methodologies, this opportunity is for you. As a Machine Learning Engineer, you'll be a key player in our data-driven initiatives, shaping the future of investment decision-making with innovative approaches.

Investment Science’s initiatives encompass the development, enhancement, and systematization of investment decisions across various asset classes in both public and private markets. Our recent focus has been on driving critical decisions throughout the investing lifecycle, including capital allocation and security selection, using proprietary forecasts powered by advanced techniques such as machine learning, natural language processing, and generative AI.

As a valued team member, you will collaborate with a diverse group of investors, researchers, and engineers. Your responsibilities will include leading or supporting the onboarding, engineering, and evaluation of new datasets; constructing and assessing predictive features; developing and implementing AI models and forecasts; and ensuring the productionalization and systematization of research outcomes and investment portfolios.

The Opportunity

• Develop and Enhance ML Models Build, improve, deploy, and maintain machine learning models to predict future fundamentals. Drive impact by leveraging and advancing AI/ML capabilities to enhance investment research and improve forecasting accuracy.
• Advance Investment Research: Contribute to the development and application of investment research and frameworks, combining investing priors with data and advanced analytical techniques to solve key problems in the areas of capital allocation and security selection.
• Alpha Generation: Apply an alpha generation mindset to data and feature engineering, prediction modeling, and portfolio construction.
• Collaboration: Thrive in a fast-paced setting, working closely with investment professionals, researchers, engineers, and subject matter experts across the organization.

Requirements

Background:

• Bachelor's or Master’s degree in Computer Science, Data Science, Statistics or related quantitative fields.
• Two to three years of full-time experience.
• Investment experience is an asset. Alternatively, strong demonstrated interest in investing and financial data and decision-making.

Core Technical:

• Exceptional programming skills are required, preferably in Python, but also open to C/C++, C#, or Java.
• Experience developing, implementing, and deploying scalable ML models and pipelines in research and production environments, including experience with ML frameworks and libraries (e.g., scikit-learn).
• Experience with big data processing frameworks such as PySpark is an asset.
• NLP proficiency and/or experience utilizing LLMs/Foundation Models is advantageous.

Mindset:

• Demonstrated growth mindset and intellectual curiosity, with a passion for creating value for the Fund and continuously improving one’s technical capabilities (e.g., researching various AI techniques).
• Strong critical thinking, analytical and problem-solving skills.
• Excellent written and verbal communication skills, with an interest in developing people management and stakeholder influencing skills.
• Ability to work & thrive in an entrepreneurial environment and be a self-starter.
• Commitment to exemplifying CPP Investments' Guiding Principles of Integrity, Partnership, and High Performance.