Asset Management - Quantitative Analyst - Artificial Intelligence & Machine at JPMC Candidate Experience page

Pudong, Shanghai, China -

Full Time

Start Date

Immediate

Expiry Date

07 Jun, 26

Salary

0.0

Posted On

09 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Artificial Intelligence, Machine Learning, Large Language Models, Quantitative Analysis, Investment Research, Alpha Generation, Portfolio Optimization, Risk Management, Deep Learning, Natural Language Processing, Python, R, Matlab, SQL, SPARK, Time-Series Analysis

Industry

Financial Services

Description

As a Quantitative Analyst at China Asset Management with a focus on Artificial Intelligence (AI), machine learning (ML), and large language models (LLMs), you will drive the integration of advanced AI techniques into investment research and strategy development. Job Summary We are looking for a highly skilled Quantitative Analyst to develop and implement cutting-edge quantitative models that leverage AI, machine learning, and LLMs for investment research, alpha generation, portfolio optimization, and risk management. The ideal candidate will have a strong background in applying these technologies to financial markets, including sentiment analysis from unstructured data (e.g., news, earnings calls, social media), predictive modeling, and strategy enhancement. 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, backtesting 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. J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs [https://careers.jpmorgan.com/us/en/how-we-hire/faqs] for more information about requesting an accommodation. J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.

Responsibilities

The Quantitative Analyst will research and implement quantitative investment models using machine learning and deep learning across various asset classes, applying advanced AI methods like LLMs and NLP to extract insights from textual data for sentiment analysis and strategy enhancement. Responsibilities also include collaborating with teams to implement models in production, optimize portfolios, manage risk, and staying current with AI/ML advancements in investment research.