Description:
In this role, the incumbent will collaborate closely with investment teams to perform data analytics that support strategic business decisions. They will be responsible for maintaining and enhancing data solutions to meet evolving business analytics requirements. They will also develop the skills and expertise needed to analyze large datasets and create innovative methods for interpreting and visualizing information.
Who You'll Work With
You’ll join the Data Science & AI team, which is a multidisciplinary group of data scientists, data analytics specialists, data engineers and data visualization experts. In this team, you will work in a highly collaborative environment to deliver high quality data solutions. You’ll also work directly with our internal business team clients (Capital Markets, Private Investment, and more). You will report to Senior Manager, Advanced Analytics.
What You'll Do
Technical Responsibilities
- Build and maintain data models with a strong understanding of data structures, relationships, and integration across systems.
- Contribute to the design and evolution of data foundations and architecture to support long-term analytical needs.
- Maintain, monitor, and continuously improve the data science ecosystem by implementing CI/CD pipelines, applying version control practices (Git, GitHub), and leveraging automation tools (e.g., GitHub Actions) to ensure scalable, reliable, and collaborative development.
- Collaborate on data engineering activities such as data preparation, transformation, and integration.
- Assist in building predictive models and statistical analyses to support business forecasting and scenario planning.
Business & Analytics Responsibilities
- Visualize data, metrics, and trends to produce actionable insights, and collaborate with stakeholders on implementation.
- Support business initiatives by developing, maintaining, and enhancing critical reports, dashboards, and data solutions.
- Analyze and interpret business KPIs using data science and advanced analytics techniques.
- Partner directly with business teams to embed data-driven insights into decision-making processes.
- Evaluate alternative and external data sources, leveraging quantitative methods for potential business applications.
- Conduct research into emerging methods, tools, and solutions for data science and analytics challenges.
- Stay current with industry developments, emerging technologies, and trends in data and analytics, as well as business domain knowledge.
What You'll Need
- Programming: Python, SQL
- Data Visualization: Power BI, Tableau
- Data Management: Data modelling, data engineering concepts, ETL/ELT processes
- Version Control & Automation: Git, GitHub, CI/CD pipelines, GitHub Actions
- Databases & Cloud Platforms: Snowflake, SQL Server
- Analytics & Modelling: Statistical analysis, predictive modelling, machine learning methods
- Business Domain: Financial KPIs, investment analytics, portfolio and risk analysis
- Bachelor’s degree in Mathematics, Statistics, Computer Science, Engineering, or other quantitative discipline.
- Minor or advanced degree in Finance, Economics, or related field is an asset.
- 3+ years of experience in business-oriented data science and analytics, ideally within investment or financial services.
- Proficiency in Python and SQL, with experience in data visualization tools such as Power BI or Tableau.
- Knowledge of data modelling, data engineering concepts, and integration across multiple systems.
- Familiarity with CI/CD pipelines, version control (Git, GitHub), and automation tools (e.g., GitHub Actions).
- Ability to apply statistical methods, predictive modelling, and advanced analytics to solve business problems.
- Understanding of investment decision-making processes, financial KPIs, and portfolio analytics is an asset.
- Strong communication skills, with the ability to explain complex ideas to both technical and non-technical stakeholders.
- Demonstrated ability to work collaboratively in cross-functional teams across investments and technology.
- Self-motivated, curious, and able to conduct independent research.
- Passion for data analysis, problem solving, and enabling data-driven decision making.