Wealthsimple
Senior Data Science Manager
🌎
Canada
1 month ago
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Job Description
Your career is an investment that grows over time!
Wealthsimple is on a mission to help everyone achieve financial freedom by reimagining what it means to manage your money. Using smart technology, we take financial services that are often confusing, opaque and expensive and make them transparent and low-cost for everyone. We’re the largest fintech company in Canada, with over 4 million users who trust us with more than $50 billion in assets.
Our teams ship often and make an impact with groundbreaking ideas. We're looking for talented people who keep it simple and value collaboration and humility as we continue to create inclusive and high-performing teams where people can be inspired to do their best work.
About the team:
The Data Science & Engineering (DSE) team consists of analytics engineers, data scientists, and software engineers with diverse educational backgrounds such as math, operations research, economics, computer science, engineering and business. The team is responsible for enabling data-driven decision making and building data products at Wealthsimple.
We achieve these goals by:
- Building a high quality and scalable state-of-the-art data warehouse that powers all decision making
- Leveraging machine learning and algorithms to help Wealthsimple build smarter financial products
- Using decision science to understand the cause and effect of our business decisions
About the role:
At Wealthsimple, our Investing teams are the driving force behind Self Directed Investing and Managed Invest products. We are hiring for an experienced data science leader to join our Investing leadership team. This individual will work on some of the highest priority initiatives at Wealthsimple, ensuring that our product strategy is well grounded in data and analytics.
Some of your projects may include, but not be limited to:
- Analyze user behaviour to understand how clients interact with the product
- Develop and implement experiments to evaluate new features and product changes
- Apply causal inference methods in non-experimental settings
- Use machine learning algorithms to segment users and personalize product experiences
- Develop and maintain core data models and pipelines to ensure data quality and accessibility
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