Research Intern (London)

Voleon

Greater London

On-site

GBP 26,000 - 38,000

Full time

5 days ago
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Benefits offered by this job

Competitive compensation
Technology talks
Catered lunches

Job summary

Voleon in London invites a Research Intern to work with world-class ML scientists and statisticians on large-scale data sets and automated trading components. You will contribute to novel research at the frontier of statistical ML and collaborate with top professionals in finance and technology.

Ideal candidates are graduate students in quantitative fields aiming for a PhD by 2027/2028, with strong ML background, publication record, and programming skills.

Qualifications

  • Graduate student in a quantitative discipline aiming for a PhD by Dec 2027 or 2028.
  • Strong background in modern statistical methods and ML.
  • Contributions to multiple top-tier publications.
  • Programming experience and interest in software development techniques.
  • Authorization to work in the United Kingdom.

Responsibilities

  • Work on complex problems with a research team that has published in top-tier venues.
  • Apply ML and statistics to challenging problems in finance and ML research.
  • Develop predictive models and components of automated trading systems.
  • Engage in data preparation, model development, and production software integration.

Skills

Statistics
Machine learning
Programming
Data analysis

Education

PhD candidate in quantitative discipline

Tools

Python
Git

Job description

the company is a technology company that applies state-of-the-art AI and machine learning techniques to real-world problems in finance. For nearly two decades, we have led our industry and worked at the frontier of applying AI/ML to investment management. We have become a multibillion-dollar asset manager, and we have ambitious goals for the future.

Your colleagues will include internationally recognized experts in artificial intelligence and machine learning research as well as highly experienced finance and technology professionals. In addition to our enriching and collegial working environment, we offer highly competitive compensation and benefits packages, technology talks by our experts, a beautiful modern office, daily catered lunches, and more.

As a Research Intern, you will work directly with world-class machine learning scientists, statisticians, and technical innovators. You will be at the forefront of modern statistical machine learning innovation, applying novel research to large, high-dimensional data sets. This role is a means to make a difference: together with other Members of Research Staff, you will have an enduring impact on our field.

Responsibilities
  • Work on complex problems with a research team that has collectively published hundreds of academic articles in top-tier venues on machine learning, systems, and theory
  • Apply expertise in a technical field such as statistics, mathematics, computer science, engineering, or operations research, to challenging problems in a burgeoning technical field
  • Develop predictive models and other components of automated trading systems
  • Engage in work ranging from data preparation to model development to production software implementation
Requirements
  • Current graduate student in a quantitative discipline, expecting to attain a Ph.D. degree in December 2027, or anytime in 2028
  • Background in modern statistical methods and machine learning
  • Key contributions to multiple top tier publications
  • Programming experience and interest in software development techniques
  • Authorization to work in the United Kingdom

Interest in financial applications is essential, but experience in finance is not a primary factor in our hiring

Equal Opportunity EmployerThe the company Group is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.

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