Investment Analytics Engineering Intern - Data & AI

Connor, Clark & Lunn Financial Group (CC&L); Vancouver; On-site; CAD 55,000 - 65,000; Full time

Benefits offered by this job

Annual performance bonus

Inclusive recruitment process

Job summary

Connor, Clark & Lunn Financial Group (CC&L) invites an Intern to join the Investment Analytics Engineering team in Canada. You will support analytics discovery, help modernize the analytics platform, and collaborate with project leads to generate insights for affiliates' investment processes.

You will work on scalable analytics such as performance, attribution, risk, and data visualizations, while applying AI methods to enhance modelling and data querying.

Qualifications

• Pursuing a bachelor’s or master’s degree in Finance, Mathematics, Data Science, Computer Science, or a related quantitative field.
• Interest in asset management and investment analytics; prior exposure is a bonus but not required.
• Familiarity with Python, R, SQL or PowerBI is an asset and helpful for analytics work.
• Strong data querying and analytical mindset with curiosity to learn.

Responsibilities

• Support analytics discovery and platform modernization efforts.
• Work with project leads to develop scalable investment analytics and visualizations.
• Assist with data engineering tasks to strengthen data pipelines and automate workflows.
• Explore AI applications for financial modelling, anomaly detection, and NLP querying.

Skills

Python

R

SQL

PowerBI

Data storytelling

Analytical thinking

Collaboration

Problem solving

Education

Finance/Math/Data Science/Cs degree

Tools

Python

R

SQL

PowerBI

Job description

Connor, Clark & Lunn Financial Group (CC&L) invites an Intern to join the Investment Analytics Engineering team in Canada. You will support analytics discovery, help modernize the analytics platform, and collaborate with project leads to generate insights for affiliates' investment processes.

You will work on scalable analytics such as performance, attribution, risk, and data visualizations, while applying AI methods to enhance modelling and data querying.