WQBRAIN Research Intern at WorldQuant

Hanoi, Hanoi, Vietnam

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Not SpecifiedCompensation

InternshipExperience Level

InternshipJob Type

UnknownVisa

Finance, FinTechIndustries

Requirements

Familiarity and competence in using BRAIN, or BRAIN Research Consultant preferred
Possess or expect a Bachelor’s degree or advanced degree in engineering, science, mathematics, finance or any other related field that is highly analytical and quantitative from a leading university
Have a strong interest in learning about worldwide financial markets
Possess good communication and presentation skills in English

Responsibilities

Create and develop Alphas and other utilization algorithms on BRAIN
Conduct research on academic quantitative finance literature
Assist BRAIN researchers in identifying and designing new research domains. Generate ideas to grow the domain
Analyze current functionalities on BRAIN, provide solutions and recommendations to the BRAIN team
Design and test new functionalities and datasets on BRAIN
Assist BRAIN Strategy and Operations country heads and other Business Development partners to enhance & implement BRAIN business strategy for user and consultant acquisition and engagement

Skills

Key technologies and capabilities for this role

Quantitative ResearchMachine LearningAIMathematicsComputational MethodsData AnalysisFinancial Strategies

Questions & Answers

Common questions about this position

What is the salary for the WQBRAIN Research Intern position?

This information is not specified in the job description.

Is the WQBRAIN Research Intern role remote or office-based?

This information is not specified in the job description.

What qualifications are required for the WQBRAIN Research Intern role?

Candidates should have familiarity and competence in using BRAIN or as a BRAIN Research Consultant, and possess or expect a Bachelor’s degree or advanced degree in engineering, science, mathematics, finance, or another highly analytical and quantitative field.

What is the company culture like at WorldQuant?

WorldQuant pairs academic sensibility with accountability for results, encourages open thinking about problems, balancing intellectualism and practicality, and values excellent ideas from anyone, anywhere, with an attitude of continuous improvement.

What makes a strong candidate for the WQBRAIN Research Intern position?

Strong candidates demonstrate outstanding intellectual talent and familiarity with BRAIN, along with a degree in a highly analytical field like engineering, science, mathematics, or finance.

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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