Value Addition Research Scientist

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 research scientist to join the Value Addition team. Reporting directly to the firm’s Chief Strategy Officer, the individual will be part of a dynamic group of researchers and business leaders on the cutting edge of quantitative finance. Together they enable the firm to measure the value added at each layer of the investment process — from data to strategies and beyond — and reflect WorldQuant’s ethos of optimization. The team has created a holistic value addition system — providing a database to portfolio managers for strategy creation, dashboards used by researchers to assess their performance and customized analytical reports for senior management — leading to increased efficiency, focus and growth across the organization.

As a research scientist at WorldQuant, you will make a cross-functional impact and have the opportunity to:

• Contribute code implementations and maintain existing code for evaluation models
• Build mathematical (optimization/machine learning/statistical) models for investment entity evaluation and resource allocation across them
• Operate recurring entity evaluation pipelines
• Access a variety of software and hardware resources and state-of-the-art systems
• Create on-demand analytical reports and produce business commentary on investment entities

What You’ll Bring

• Willingness to explain and defend employed models, their interpretation and business value to the team and stakeholders
• Graduate-level research experience in a quantitative field
• Ability to run full-length research work: from business idea, through data collection and model implementation, to tangible business data produced at regular frequencies
• Excellent knowledge of Python, NumPy and Pandas
• C++ knowledge is a plus
• Passion for reasonable accuracy–explainability trade-offs in models
• Experience with at least one of: Intel MKL, JAX, PySpark, Dask, Ray, ClickHouse, Vertica, Arrow
• Experience working with “large” datasets (>100G CSV)
• Experience with at least one of: XGBoost, LightGBM, CatBoost, Tensorflow, PyTorch, MOSEK, CVXPY
• Graduate-level calculus, linear algebra, optimization and machine learning algorithms knowledge
• Machine learning models research and operations experience

Our Benefits

• Perks: Employee discounts for gym memberships, wellness activities, healthy snacks, casual dress code
• Training: learning and development courses, speakers, team-building off-site
• 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
• Employee resource groups

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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.