Asset Management - AI Quant Analyst - Artificial Intelligence & Machine Learning Focus

Key Responsibilities

• Research and implement quantitative investment models using machine learning and deep learning techniques across equities, fixed income, commodities, and other asset classes. 
• Apply advanced AI methods, including large language models and natural language processing (NLP), to extract insights from textual data for sentiment analysis, event detection, and alternative data integration.
• Conduct rigorous research to identify new alpha signals, improve existing strategies, and enhance predictive accuracy in investment decision-making.
• Collaborate with portfolio managers, research analysts, and engineers to implement models in production, optimize portfolios, and manage risk exposures.
• Stay current of the latest advancements in AI/ML/LLMs and their applications in investment research and management, contributing to the firm's innovation in investment strategies and capabilities.
• Present research findings and model results to investment teams, senior management, and other stakeholders

 

Qualifications

• A master’s or PhD degree in a quantitative discipline such as mathematics, statistics, computer science, physics, or engineering and with exposures in asset pricing, financial economics, statistical analysis, macroeconomics or econometrics, and stochastic modeling and scenario analysis (CFA or equivalent)
• 3+ years of experience in quantitative research or analysis within asset management, hedge funds, investment banking, or a similar financial environment.
• Proven expertise in machine learning, deep learning, and AI techniques, with hands-on experience applying LLMs/NLP to financial or textual data.
• Strong coding and data analysis skills are required. Python experience (additional experience in R or MATLAB, SQL, SPARK and familiarity with large financial databases). 
• Experience with statistical modeling, time-series analysis, back-testing frameworks, and big data tools.
• Knowledge of financial markets, asset pricing, portfolio theory, and risk management.
• Excellent problem-solving abilities, attention to detail, and the capacity to work independently as well as in a collaborative team environment.
• Strong and effective communication skills (both verbal and written), especially for explaining complex quantitative concepts to non-technical stakeholders.
• Attainment of all necessary regulatory licenses (or any other licenses / qualifications as required) for carrying out Asset Management and other regulated activities

 

Preferred Skills

• Experience with generative AI, diffusion models, or agentic workflows in finance.
• Familiarity with alternative data sources and their integration into investment processes and strategies.

• Track record of developing profitable trading signals or models using ML/LLM techniques.