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Data Scientist III - Financial Crimes Modeling - TD
Data Scientist
Senior data scientist focused on building predictive models to detect and prevent financial crimes, leveraging Python, machine learning, and advanced statistical techniques to analyze large transactional datasets.
About the role
Key Responsibilities
- Design, develop, and deploy machine learning models to identify and mitigate financial crime risks across banking products.
- Extract, transform, and analyze large volumes of transactional data using SQL and Python, ensuring data quality and integrity.
- Collaborate with cross‑functional teams to define fraud detection metrics, feature engineering pipelines, and model evaluation frameworks.
- Interpret model outputs and communicate actionable insights to stakeholders, supporting risk mitigation strategies.
- Maintain model documentation, version control, and reproducibility in a regulated environment.
Requirements
- 5+ years of experience in data science or analytics within financial services, with a focus on fraud or risk modeling.
- Proficiency in Python (pandas, scikit‑learn, XGBoost) and SQL for data manipulation and feature engineering.
- Strong statistical background and experience with supervised/unsupervised learning techniques.
- Experience deploying models to production (e.g., Docker, cloud services) and monitoring model performance.
- Excellent communication skills and ability to translate complex analytics into business recommendations.
Skills
pythonmachine learningsql