Data Scientist

6 month contract + possibility for conversion to permanent

Hybrid 5x per month in Toronto

Pay: $40-47/hr T4 | $45-52/hr incorp

Must-Haves:

• 3-5 years of strong experience with Python, PySpark and SQL.
• 2+ years of experience developing AI/ML models.
• Proficiency in end-to-end model development and validation.
• Strong ability to manipulate and analyze large datasets to extract meaningful insights.
• Strong presentation and communication skills to summarize and present complex analyses to both technical and business stakeholders.

Nice to Have:

• Experience with SAS for model development and validation.
• Experience with fraud detection and AML models, particularly involving transaction and behavioral data.
• Knowledge of risk and market risk, particularly in the context of financial services.

Overview:

We are looking for a detail-oriented and driven Intermediate Data Scientist to join our team. In this role, you will be responsible for analyzing large datasets, building AI/ML models, and extracting insights to support fraud detection and anti-money laundering (AML) efforts. You will spend a significant portion of your time working with data, manipulating it, and summarizing analysis to present results in an accessible manner to both technical and business stakeholders.

Responsibilities:

• Spend 50%-70% of your time working with large datasets to extract, manipulate, and clean data for model development.
• Develop and validate AI/ML models, focusing on fraud detection and AML, using Python, SAS, and SQL.
• Conduct in-depth data analysis, summarizing findings and generating insights that can be easily communicated to both technical and business audiences.
• Work closely with cross-functional teams to integrate models and provide continuous monitoring and validation.
• Measure and assess the performance of models, applying statistical and probability-based techniques.
• Focus on identifying behavioral patterns in transactional data, such as credit card statements and balances, to build models that detect fraud or money laundering.
• Ensure that models are built, tested, and validated according to industry best practices.