Quantitative Researcher, Quant Macro at WorldQuant

Taipei, Taiwan

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Finance, Quantitative FinanceIndustries

Requirements

Degree (BEng, MSc, or PhD) from a top university in a field such as Mathematics, Computer Science, Physics, Electrical Engineering, Financial Engineering, or a related analytical field
Expertise in quantitative research toolkit, including data processing, modeling, and visualization in Python, R, or C++
High GPA and academic grades
Research mentality: deep thinker, creative, strong work ethic, persevering, smart, and self-starter
Strong interest in learning about worldwide financial markets
Strong communication skills in English
Research scientist mindset, motivated by unsolved challenges

Responsibilities

Develop computer-based models to predict movements of global financial markets
Specialize in futures contracts or Macro asset classes – Commodities, FX, Interest Rates, or Equity Indices
Employ tested processes to identify high-quality predictive signals
Contribute to the firm's research platform focused on generating alphas

Skills

Key technologies and capabilities for this role

PythonRC++Data ProcessingQuantitative ModelingVisualizationSignal ResearchFinancial Markets

Questions & Answers

Common questions about this position

What technical skills are required for the Quantitative Researcher role?

Expertise in quantitative research toolkit, including data processing, modeling, and visualization in Python, R, or C++ is required.

What education is needed for this position?

A degree (BEng, MSc, or PhD) from a top university in fields such as Mathematics, Computer Science, Physics, Electrical Engineering, Financial Engineering, or a related analytical field is required, along with a high GPA.

What is the location or work arrangement for this role?

This information is not specified in the job description.

What is the company culture like at WorldQuant?

WorldQuant fosters a culture that pairs academic sensibility with accountability for results, encouraging open thinking and continuous improvement, with research at its core and a focus on individuals with a research scientist mindset motivated by unsolved challenges.

What personal qualities make a strong candidate for this role?

The ideal candidate has a research mentality as a deep thinker, creative individual with a strong work ethic, perseverance, intelligence, and self-starter qualities, along with strong interest in financial markets and excellent English communication skills.

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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