Quantitative Research Intern at WorldQuant

Hanoi, Hanoi, Vietnam

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Not SpecifiedCompensation

InternshipExperience Level

InternshipJob Type

UnknownVisa

Finance, Quantitative FinanceIndustries

Requirements

Preferred: BS (Hons), MS or PhD in Computer Science, Artificial Intelligence, Computer Vision, Machine Learning, Data Science, or Mathematics. Considered: Exceptional candidates without an advanced degree
Proficiency in at least one of the following programming languages: Python, C++, or Java
Bonus: Deep learning or prompt engineering coursework or experience
Bonus: Participation in national and international mathematics or programming competitions
Good understanding of the investment research process to create computer-based models that predict movements of global financial markets
Strong interest in learning about stock markets and other capital markets
Self-starter with a research scientist mindset
Creative and persevering deep thinker motivated by unsolved challenges

Responsibilities

Assist with daily research and analysis tasks
Script for monitoring and alpha signal analysis
Contribute to the development of computer-based models for predicting financial market movements
Learn about stock markets and other capital markets

Skills

Key technologies and capabilities for this role

Quantitative ResearchFinancial ModelingPredictive SignalsAlpha GenerationData AnalysisMathematical ModelingInvestment Research

Questions & Answers

Common questions about this position

What is the salary for the Quantitative Research Intern position?

This information is not specified in the job description.

Is this internship remote or does it require office work?

This information is not specified in the job description.

What programming skills are required for this role?

Candidates must have proficiency in at least one of the following: Python, C++, or Java.

What is the company culture like at WorldQuant?

WorldQuant is built on a culture that pairs academic sensibility with accountability for results, encouraging employees to think openly about problems, challenge conventional thinking, and demonstrate intellectual horsepower and outstanding talent.

What makes a strong candidate for this Quantitative Research Intern role?

Strong candidates will be self-starters with a research scientist mindset, creative and persevering deep thinkers motivated by unsolved challenges, preferably holding a BS (Hons), MS, or PhD in fields like Computer Science, AI, Machine Learning, Data Science, or Mathematics, along with programming proficiency.

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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