Data Scientist (L3)

Design and deploy agentic AI systems (tool-use, multi-step reasoning) and RAG-based LLM pipelines to automate and enhance leveraged loan and CLO trading workflows, including document interpretation and process automation. Own end-to-end production ML/LLM pipeline delivery for leveraged loan and CLO use cases from data preparation and model development to CI/CD deployment, scalable inference, monitoring, drift detection, and automated retraining. Establish and enforce data governance for leveraged loan and CLO workflows by implementing data quality controls (validation rules, reconciliation, anomaly detection), lineage and audit trails, access/permission standards, and schema/versioning discipline to ensure regulatory-ready transparency and internal risk control compliance. Partner cross-functionally (Product, Engineering, Data Engineering, Sales) to define schemas, reference standards, and requirements for new leveraged loan and CLO capabilities; translate needs into shipped platform features and incremental releases. Provide technical guidance to junior data scientists on fine-tuning/domain adaptation and best practices for safe, compliant model deployment in leveraged loan and CLO applications. Perform sensitivity/scenario analysis on capital markets datasets to support decisioning, product insights, and model performance improvements for leveraged loan and CLO markets. Requirements:Masters degree in Business Analytics, Data Science or Statistics, plus 2 years of experience in the position offered or as a Data Analyst, Data Scientist, or Quantitative Researcher in financial technology or electronic trading.* *All required experience must have included: driving product-level investment decisions for financial product offerings supporting high-frequency agents and electronic trading; performing sensitivity and scenario analysis; building scalable cloud-based data infrastructure on AWS (or similar); architecting end-to-end ML pipelines using Python, SQL, and Spark; extracting insights from large/complex datasets and building predictive/stochastic modeling frameworks for rapidly evolving markets; translating capital markets requirements into production-ready data architectures; establishing data governance standards (quality controls, lineage, access controls, and auditability), designing and deploying agentic AI systems with tool-use, multi-step reasoning, and retrieval-augmented generation (RAG) workflows; and applying data science and machine learning solutions to leveraged loan and CLO trading workflows, including document interpretation, process automation, and market analysis. This role entails hybrid work, with four days required in our New York, NY office and the fifth workday may be spent telecommuting from the workers home in the U.S. #LI-DNI. To apply: E-mail rsum to: [email protected].