AI/ML Data Scientist, Fixed-Income Structured Products - VP
Location: New York
Type: Full-time
Overview
We are seeking an AI Research Data Scientist to join a high-performing Mortgage Quant team focused on structured products and fixed-income markets. The ideal candidate has a strong academic background in deep learning and quantitative sciences, combined with hands-on experience developing advanced machine learning models for mortgage and asset-backed securities. This role offers the opportunity to lead end-to-end research, model development, and production deployment within a data-driven investment environment.
Responsibilities
• 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. Qualifications
• 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. Technical Skills
• 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.
