Key Responsibilities
- Design, prototype, and validate machine learning models for credit and fraud risk assessment.
- Collaborate with cross‑functional teams to translate business problems into data‑driven solutions.
- Conduct research on novel algorithms, including deep learning and graph‑based methods, to improve detection accuracy.
- Deploy models into production environments, ensuring scalability, robustness, and compliance with regulatory standards.
- Communicate findings and recommendations to stakeholders through clear visualizations and technical reports.
Requirements
- Ph.D. or Master’s in Computer Science, Statistics, or related field with strong research background.
- Proven experience in Python, machine learning libraries (scikit‑learn, TensorFlow, PyTorch) and SQL.
- Deep understanding of fraud detection, credit risk modeling, and feature engineering.
- Excellent problem‑solving skills and ability to work independently in a fast‑paced environment.
- Strong written and verbal communication skills for presenting complex ideas to non‑technical audiences.
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