TWG Global
VP, Data Science / Machine Learning Lead - Capital Markets & Fixed Income
$290K–$300K
New York, New York · Director · Full-time · Posted Jul 7, 2026
- Experience
- 8+ years; significant financial services experience, preferably in investment banking, asset management, fixed income, or credit markets.
- Education
- Master’s or PhD in Computer Science, Machine Learning, Statistics, Financial Engineering, or a closely related discipline preferred.
About the job
Build and deploy production AI systems and LLM-powered products that automate investment banking and capital markets workflows, especially in fixed income and credit.
What you'll do
- Design, ship, and maintain production LLM pipelines and reusable AI platform components.
- Develop AI-driven analytics for fixed income and credit markets by combining quantitative signals with unstructured data.
- Work with business stakeholders and technical teams to deliver, evaluate, and improve AI products; mentor engineers and data scientists.
What they ask for
- 8+ years building and deploying production software or ML systems, with significant financial services experience.
- Proven record leading AI/ML projects from ideation to production and building production LLM applications, including RAG, orchestration, structured output parsing, and evaluation.
- Expertise in at least one of supervised/unsupervised learning, statistical modeling, NLP, or document information extraction.
- Strong investment banking and capital markets knowledge; proficiency in Python and modern AI/ML tools, with MLOps and cloud infrastructure knowledge.
Nice to have
- Experience with enterprise data and AI platforms such as Palantir Foundry.
- CFA or FRM certification; client-facing financial services experience.
Pay
$290,000-300,000 USD base pay
Tools
Python, PyTorch, scikit-learn, LangChain/LangGraph, AWS, GCP, Palantir Foundry
AI focus
Build production LLM systems and AI-driven financial analytics, including document extraction, retrieval-augmented generation, workflow orchestration, evaluation, and responsible deployment.
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