Intermediate Data Scientist

2 days ago

Toronto Ontario, Ontario, Canada Insight Global Full time

6 month contract

Hybrid 5x per month - Toronto

Pay range: $40-50/hr


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.


Must-Haves:

  • 3-5 years of experience with Python 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.
  • Experience with fraud detection and AML models, particularly involving transaction and behavioral data.
  • 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.
  • Knowledge of risk and market risk, particularly in the context of financial services.

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