AI/ML Data Scientist, Fixed-Income Structured Products - VP
Type: Full-time
- Lead AI and machine learning research initiatives across mortgage and structured product strategies.
- Develop, refine, and productionize predictive models for prepayment, default, and valuation of MBS and ABS.
- Build deep learning and transformer-based architectures that improve forecasting accuracy and risk assessment.
- Design scalable multi-agent or automated systems for valuation workflows and investment analytics.
- Create and maintain NLP-driven tools, including AI news analytics, LLM-based applications, and market signal generation platforms.
- Collaborate with portfolio managers, quantitative researchers, and technology teams to translate research into actionable insights and production systems.
- Stay current with the latest advances in deep learning, language models, and applied AI relevant to financial markets.
- Ph.D. or Masters in Computer Engineering, Electrical Engineering, Computer Science, Applied Mathematics, Physics, or a related quantitative field. Focus on deep learning is strongly preferred.
- Undergraduate degree with strong coursework or dual majors in applied math, physics, engineering, or similar quantitative disciplines.
- Experience in fixed-income analytics, especially within mortgage markets, structured products, or related asset classes.
- Proven background in developing transformer-based, deep learning, or hybrid ML systems for forecasting, valuation, or risk modeling.
- Prior experience building NLP, LLM, or QA systems for real-world financial or enterprise applications.
- Experience deploying machine learning models into production environments and working with cross-functional stakeholders.
- Previous exposure to financial institutions, hedge funds, or machine learning centers of excellence is a plus.
- Programming: Python, SQL.
- Frameworks and Tools: PyTorch, TensorFlow, Hugging Face, LangChain, and related ML or NLP libraries.
- Strong understanding of model training, optimization, data engineering workflows, and cloud or distributed computing environments.
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