Data Science Specialist: Fraud Detection and AML
5 days ago
Job Summary
We are seeking a skilled Data Science Specialist to join our team at Insight Global. In this role, you will work closely with cross-functional teams to develop and validate AI/ML models for fraud detection and anti-money laundering (AML) efforts.
About the Role
This is a hybrid 5x per month position in Toronto, with an estimated salary range of $40-50/hour. You will spend a significant portion of your time working with large datasets to extract, manipulate, and clean data for model development.
Key Responsibilities
- Data Analysis: Spend 50%-70% of your time analyzing large datasets to extract meaningful insights.
- Model Development: Develop and validate AI/ML models using Python, SAS, and SQL, focusing on fraud detection and AML.
- Insight Generation: Conduct in-depth data analysis, summarizing findings and generating insights that can be easily communicated to both technical and business audiences.
- Cross-Functional Collaboration: Work closely with cross-functional teams to integrate models and provide continuous monitoring and validation.
- Performance Assessment: Measure and assess the performance of models, applying statistical and probability-based techniques.
- Fraud Detection: Focus on identifying behavioral patterns in transactional data to build models that detect fraud or money laundering.
Requirements
- Python Experience**: 3-5 years of experience with Python and SQL.
- AI/ML Model Development**: 2+ years of experience developing AI/ML models.
- Proficiency in End-to-End Model Development and Validation.
- Large Dataset Manipulation**: Strong ability to manipulate and analyze large datasets to extract meaningful insights.
- Fraud Detection and AML Models**: Experience with fraud detection and AML models, particularly involving transaction and behavioral data.
- Presentation and Communication Skills**: Strong presentation and communication skills to summarize and present complex analyses to both technical and business stakeholders.
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