Data Scientist, Investment Data

We are looking for a Data Scientist to join our Quantitative Equity Team! We are a high-performance team embedded in a top-performing quant fund that manages over $75 billion in financial assets. We are dedicated to the mission‑critical operation of our investment engine—a well‑oiled machine responsible for generating key investment insights that drive trades. Leveraging data analytics, finance knowledge and cutting‑edge technology, we aim to ensure that new and useful data products are continuously ready for research.

Do you love the idea of evaluating and integrating large proprietary data sources into valuable applications? Are you a wizard at transforming ‘messy reality’ into high quality data assets? As a core member of our team, you will collaborate with investment and data experts to tackle challenging problems, and to continuously expand our capabilities in the data space. Based on the west coast in Vancouver, we value mentorship, collaboration, and growth in a supportive and innovative environment.

Join us to make a significant impact on our investment outcomes and overall success.

This is an exciting full‑time role for individuals who are enthusiastic about learning the quantitative equity investment management business and excited to tackle a broad range of investment, mathematical, and technology challenges.

You will start with a comprehensive training program, learning about various elements of quantitative equity investment management and our investment process.

You will then continue to a specialized role utilizing data science, machine learning, AI, and process engineering skillsets to accelerate the Quantitative Equity Team’s data preparation and modelling functions. Your specialized role will involve building, scaling, managing and evaluating our integrated data model, in direct support of alpha research.

You will be supported with coaching and mentorship from senior members of the team, and we will create the conditions for you to grow your career steadily over time. This is an opportunity for ambitious individuals to drive their career growth in the data.

While we take the approach of tailoring the career path to individual strengths, we expect successful candidates for this role to develop excellence in the following tasks:

• Develop and continuously improve practical methods for named entity recognition (NER), and create knowledge graphs at scale
• Create methods, algorithms & processes to identify, link, explore and assess billions of data points.
• Test structured and unstructured data sets for data quality and reliability.
• Collaborate with quantitative researchers – support alpha projects with your data expertise.
• Collaborate with technology specialists – help trade off application performance and operational feasibility.
• Support production deployments of your work to ensure long‑term reliability
• Enable accessibility and clarity of your work through robust documentation and exposition.
• Present findings and recommendations to key stakeholders in a clear, concise, and compelling manner.

• Analytical Mindset – You think critically and creatively and are adept at balancing intuition with statistical validation.
• Data Science – You have practical experience building, validating, and enhancing performance of ML & AI models on large data sets.
• Technical Capability – You thrive working through challenging data problems that come with ambiguity and business trade‑offs.
• Financial Knowledge – Desire to acquire the financial context needed to understand your stakeholders and make good data decisions.
• Self‑Starter – You are motivated and willing to take personal accountability for quality and timeliness of work.
• Collaborative Approach – You thrive in a collaborative environment and value shared success. You proactively solicit and provide input and excel at communicating complex and technical concepts.
• Time Management – You are able to manage and prioritize multiple ongoing projects.
• Academic Training – At a minimum, an undergraduate degree in finance, mathematics, statistics, computer science, engineering, or related discipline. Completion of a relevant graduate degree is considered an asset.