Quantitative Developer - AI Implementation

Quantitative Developer - Ai Implementation

WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform.

WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement.

Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it.

The Role

WorldQuant offers an exciting opportunity for a Quantitative Strategist to join the Artificial Intelligence team. Reporting directly to the firm's Head of AI, the individual will be part of a dynamic group of researchers. Together they enable the firm to implement AI at each layer of the investment process — from data to models to strategies and execution. The team has created a platform for the end-to-end optimization of all the modules used in trading.

On the AI team at WorldQuant, you will make a cross-functional impact and have the opportunity to:

• Build AI/ML/reinforcement learning/optimization models for investment selection, optimization, execution, and more
• Implement AI solutions throughout the firm via collaboration with portfolio management, risk, and other research teams
• Access the firm's state-of-the-art data and hardware systems
• Develop the foundational software used for AI at WorldQuant

What You'll Bring

• At least 2 years of experience training AI, ML, reinforcement learning, and related models -- in the technology, academia, or quantitative trading domains
• Hands-on experience building, testing and maintaining complex software systems
• Willingness to explain and defend employed models, their interpretation and business value to the team and stakeholders
• Experience developing AI/ML algorithms and infrastructure
• Excellent knowledge of Python, NumPy and Pandas
• Experience with at least one of: XGBoost, LightGBM, CatBoost, Tensorflow, PyTorch, MOSEK, CVXPY
• Graduate-level research experience in Artificial Intelligence or related field, including work published and/or accepted to major a conference is a plus.
• C++ knowledge is a plus

Our Benefits

• Core Benefits: Fully paid medical and dental insurance for employees and dependents, flexible spending account, 401(k), fully paid parental leave, generous PTO with unlimited sick days
• Perks: Employee discounts for gym memberships, wellness activities, healthy snacks, casual dress code
• Training: learning and development courses, speakers, team-building off-site
• 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 is $150,000 to $200,000 which is specific to New York and may change in the future. When finalizing an offer we will take into consideration an individual's experience compensation organization where you will be eligible for a base salary, discretionary performance level and the qualifications they bring to the role to formulate a competitive total compensation package.

WorldQuant is an equal opportunity employer and does not discriminate in hiring on the basis of race, color, creed, religion, sex, sexual orientation or preference, age, marital status, citizenship, national origin, disability, military status, genetic predisposition or carrier status, or any other protected characteristic as established by applicable law.