Data Scientist at WorldQuant

Taipei, Taiwan

WorldQuant Logo
Not SpecifiedCompensation
Mid-level (3 to 4 years)Experience Level
Full TimeJob Type
UnknownVisa
Finance, Quantitative FinanceIndustries

Requirements

  • Master’s degree or higher in Computer Science, Electrical Engineering, or a related field from a leading university
  • Good academic record
  • Familiar with modeling, data structures, algorithms, and optimizations
  • Strong knowledge of machine/deep learning algorithms
  • Proficient in C++ and Python programming languages
  • Good communication and presentation skills in English
  • Ability to work independently and as part of a team
  • Research scientist mindset: deep thinker, creative, strong work ethic, persevering, smart, and a self-starter
  • Detail-oriented and capable of multitasking in a fast-paced environment
  • Strong interest in financial markets (a plus)
  • Participant of ACM-ICPC (a plus)

Responsibilities

  • Develop and create data to predict the movement of financial markets
  • Process, clean, and verify the integrity of unstructured data and transform it into valuable insights
  • Work closely with data science team and technologists to develop appropriate features and metrics for data processing
  • Perform analysis using machine learning techniques

Skills

Key technologies and capabilities for this role

C++PythonMachine LearningDeep LearningData StructuresAlgorithmsOptimizations

Questions & Answers

Common questions about this position

What technical skills are required for this Data Scientist role?

Candidates must be familiar with modeling, data structures, algorithms, and optimizations; have strong knowledge of machine/deep learning algorithms; and be proficient in C++ and Python.

What is the location or work arrangement for this position?

This information is not specified in the job description.

What is the salary or compensation for this role?

This information is not specified in the job description.

What is the company culture like at WorldQuant?

WorldQuant emphasizes a culture of intellectual horsepower, continuous improvement, and collaborative problem-solving.

What makes a strong candidate for this Data Scientist position?

A strong candidate holds a Master’s degree or higher in Computer Science, Electrical Engineering, or a related field from a leading university, has a good academic record, strong technical skills in ML and programming, good communication skills, and personal attributes like being a deep thinker, creative, persevering self-starter who thrives in a fast-paced environment.

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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