Lead a team of data analysts to support data annotation and labeling for machine learning models in a fintech domain. This involves developing strategies for training data optimization, identifying patterns in conversational data using NLP, and evaluating the quality of ML outputs. Requires experience in ML data annotation, data modeling, and Python.

PhD-level Quant Researcher with 0-3 years of experience in machine learning, specializing in reinforcement learning, to develop alpha signals and portfolio construction methodologies for global equity markets. Responsibilities include data analysis, model enhancement, and integrating research into production systems.

Lead Data Engineer responsible for designing, building, and maintaining scalable data pipelines and infrastructure for processing scanned document images, integrating OCR and computer vision models, and managing data storage on AWS. The role involves collaborating with data scientists and ML engineers, ensuring data quality and governance, and leading a team of data engineers.

This role focuses on developing and managing intraday liquidity analytics using AI/ML tools to forecast cash positions and manage limits within JPMorgan Chase's Corporate Treasury. The primary responsibility involves utilizing Python and ML to publish daily forecasts, derive insights from payments data, and maintain a back-testing framework for forecast accuracy. Experience with ML or time-series forecasting in financial workflows is required.

Executive Director to shape the enterprise AI data strategy, focusing on knowledge representation, ontologies, and data foundations for AI applications at scale within a financial services organization.

Senior Director of Software Engineering for AI Infrastructure Platform at JPMorgan Chase, leading multiple departments to deliver a unified AI infrastructure layer across on-premises, public cloud, and accelerated-compute vendors. Owns training and experimentation on a Kubernetes-standardized platform, acting as a design partner for architectural trade-offs and ensuring reliable, secure, and operable systems at enterprise scale. Responsibilities include standardizing AI developer workflows, building platform APIs, improving GPU availability and utilization, defining scheduling and placement strategies, embedding security and controls, driving operational excellence, and leading engineering teams.

Quantitative researcher for an Equity Derivatives team, focusing on end-to-end alpha research and strategy deployment. Responsibilities include feature engineering, building calibration/attribution/monitoring frameworks, and implementing systematic strategies using data analytics, statistical modeling, and machine learning. The role also involves leveraging AI/ML tooling to accelerate research and improve productivity.

Quantitative Equity Researcher (VP) at JPMorgan Asset Management, focusing on developing novel alpha signals and enhancing return forecasting models using advanced statistical, econometric, and machine learning techniques on large datasets. The role involves research in portfolio construction and risk management, translating insights into investment strategies, and collaborating with technology teams for model integration.