Join WorldQuant as an AI Quantitative Strategist

WorldQuant is a leader in developing and deploying systematic financial strategies across a broad range of asset classes and global markets. We aim to generate high-quality predictive signals (alphas) through our innovative research platform to exploit market inefficiencies. Our collaborative teams drive the creation of alphas and financial strategies, forming the core of a balanced, global investment platform.

About WorldQuant

WorldQuant is built on a unique culture that merges academic rigor with a strong emphasis on results. We encourage employees to think openly and challenge conventional thinking while balancing intellectualism with practicality. Great ideas can come from anyone, anywhere, and our team persistently seeks continuous improvement.

Our primary goal is to hire the best and the brightest individuals who possess exceptional talent and intellectual curiosity. We’re looking for pioneers willing to help us shape the roadmap to future success.

Role: AI Quantitative Strategist

WorldQuant offers an exciting opportunity for a Quantitative Strategist to join our Artificial Intelligence team. Reporting directly to the Head of AI Implementation, you will be part of a dynamic group of researchers at the forefront of quantitative finance. Your contributions will be crucial in implementing pioneering AI solutions at every layer of the investment process.

Responsibilities

• Build AI/ML/reinforcement learning/optimization models for investment selection, optimization, execution, and more
• Collaborate with portfolio management, risk, and other research teams to implement AI solutions firm-wide
• Leverage the firm’s state-of-the-art data and hardware systems
• Develop foundational software for AI applications at WorldQuant

Qualifications

We are looking for candidates with the following skills and experience:

• Hands-on experience training AI, ML, and reinforcement learning models
• Experience in building, testing, and maintaining complex software systems
• Ability to explain and defend models, their interpretation, and business value to team members and stakeholders
• Experience developing AI/ML algorithms and infrastructure
• Proficiency in Python, NumPy, and Pandas
• Experience with frameworks such as XGBoost, LightGBM, CatBoost, TensorFlow, PyTorch, MOSEK, and CVXPY
• Graduate-level research experience in Artificial Intelligence or a related field, with published work or conference presentations being a plus
• Knowledge of C++ is a plus

Our Benefits

At WorldQuant, we offer a comprehensive benefits package, including:

• Core Benefits: Fully paid medical and dental insurance for employees and dependents, flexible spending account, 401(k), fully paid parental leave, and generous PTO with unlimited sick days
• Perks: Employee discounts for gym memberships, wellness activities, healthy snacks, and casual dress code
• Training: Learning and development courses, speaker sessions, and team-building off-site activities
• Employee Resource Groups

WorldQuant is a total compensation organization where you will be eligible for a base salary, discretionary performance bonus, and benefits. The estimated salary range for this position in New York is $150,000 to $200,000, subject to change. We consider individual experience and qualifications when finalizing an offer to ensure a competitive total compensation package.

By submitting this application, you acknowledge and consent to the terms of the WorldQuant Privacy Policy, which explains data collection, usage, disclosure, retention, security, and your legal rights, including access, correction, and deletion. Note that rights and obligations may vary by jurisdiction.

© 2024 WorldQuant, LLC. All Rights Reserved.

WorldQuant is proud to be an equal opportunity employer. We do not discriminate in hiring based on race, color, creed, religion, sex, sexual orientation, age, marital status, citizenship, national origin, disability, military status, genetic predisposition or carrier status, or any other protected characteristic under applicable law.