WQBRAIN Researcher at WorldQuant

London, England, United Kingdom

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Not SpecifiedCompensation

Junior (1 to 2 years), Mid-level (3 to 4 years)Experience Level

Full TimeJob Type

UnknownVisa

Quantitative Finance, FinanceIndustries

Requirements

Bachelor’s degree or advanced degree in engineering, science, mathematics, finance, or a related field from a leading university
Demonstrated programming experience in one of the following: Java/C++/C/Python/MySQL/SQL Server
Knowledge of UNIX preferred
Familiarity and competence in using BRAIN, ex-VRC Research Consultant, or BRAIN Research Consultant preferred
Research scientist mindset – self-starter, creative, and persevering

Responsibilities

Create and develop Alphas and other utilization algorithms on BRAIN
Conduct research on academic quantitative finance literature
Identify and design new research domains. Generate ideas to grow the domain
Analyze current functionalities available to researchers, identify any issues with the platform, and provide solutions and recommendations to the BRAIN team
Design and test new functionalities and datasets on the BRAIN platform
Work with BRAIN Strategy and Operations country heads and other Business Development partners to enhance & implement BRAIN business strategy for user and consultant acquisition
Conduct training sessions for BRAIN users and consultants
Prepare and update training curriculum of BRAIN

Skills

Key technologies and capabilities for this role

PythonC++CJavaMySQLSQL ServerUNIXQuantitative FinanceAlgorithm DevelopmentAlpha Generation

Questions & Answers

Common questions about this position

Is this position remote?

Yes, the position is fully remote.

What programming languages are required?

Demonstrated programming experience is required in one of the following: Java, C++, C, Python, MySQL, or SQL Server. Knowledge of UNIX is preferred.

What education is required for this role?

A Bachelor’s degree or advanced degree in engineering, science, mathematics, finance, or a related field from a leading university is required.

What is the company culture like at WorldQuant?

WorldQuant has a culture that pairs academic sensibility with accountability for results, encouraging open thinking, challenging conventional ideas, continuous improvement, and valuing intellectual horsepower from anyone, anywhere.

What makes a strong candidate for this researcher role?

Strong candidates have a research scientist mindset, being self-starters who are creative and persevering, along with prior familiarity in using BRAIN, ex-VRC Research Consultant, or BRAIN Research Consultant.

WorldQuant

Quantitative asset management using algorithms

About WorldQuant

WorldQuant is a quantitative asset management firm that focuses on managing investments for institutional clients like pension funds and sovereign wealth funds. The firm uses data and predictive algorithms to analyze financial markets and identify investment opportunities. Its approach involves algorithmic trading, where mathematical models guide investment decisions. Unlike many competitors, WorldQuant encourages a culture of experimentation and innovation among its employees, allowing everyone to contribute ideas regardless of their position. The company's goal is to generate returns for its clients while maintaining a commitment to equal opportunity in the workplace.

Greenwich, ConnecticutHeadquarters

2007Year Founded

$148.5MTotal Funding

N/ACompany Stage

Quantitative Finance, Financial ServicesIndustries

1,001-5,000Employees

Benefits

Performance Bonus

Flexible Work Hours

Risks

Increased competition from AI-driven investment firms like ADIA.

Regulatory scrutiny on algorithmic trading practices is increasing globally.

Market volatility challenges the performance of algorithmic trading models.

Differentiation

WorldQuant employs over 1,000 professionals across 27 global offices.

The firm uses predictive algorithms to manage assets and generate client returns.

WorldQuant emphasizes equal opportunity, allowing all employees to contribute meaningfully.

Upsides

Increased focus on alternative data sources is gaining traction in quantitative finance.

Machine learning integration in portfolio management allows better market trend predictions.

Quantum computing offers potential for faster, complex calculations in algorithmic trading.

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