Data Scientist Intern
Brookfield Asset Management Inc. is a leading global alternative asset manager focused on property, renewable energy, infrastructure and private equity, with over $250 billion of assets under management.
Based in Toronto and New York, we have over a 100 year history as an owner and operator of real assets, offering a range of public and private investment products and services which leverage our expertise and experience. We have more than 700 investment professionals and 55,000 operating employees working in 30 countries around the world. Brookfield is publicly listed on the NYSE, TSX and Euronext Amsterdam.
The Public Securities Global Real Estate team is looking for a Data Science Intern to join our team. The intern will perform data analysis, investment model optimizations and back-testing, and assist the team in implementing new analytic tools. This internship is a unique opportunity to gain hands-on experience in data science applications related to stock investing at one of the largest global real estate investment funds. You will have a unique opportunity to work with a small investment team to improve and build new predictive models, as well as automate and optimize certain analytic processes. The right candidate will have an understanding of basic stock investment and valuation principles, and a strong “analytic curiosity” as you will be expected to come up with analytic suggestions in addition to executing on projects suggested by the team.
The position is flexible in terms hours per week.
- Professional day-to-day execution of special projects
- Help the team optimize and back-test predictive models
- Help the team automate a variety of analytic and data driven tasks
- Work with the team to design new analytic and predictive models, potentially using R, Python, or Java
- Find patterns and insights in unstructured data as it relates to real estate equity investing
- First year or higher Masters or PhD student in data science or related field (statistics, math, etc.)
- Experience with a variety of supervised and unsupervised machine learning methods
- Fluent in R and/or Python
- Basic understanding of stock valuations and equity investing
- Good communication skills, with an ability to make advanced analytics concepts accessible to non-technical team members