Quant Trader

eFinancialCareers; London; On-site; GBP 60,000 - 100,000; Full time

Job summary

An established industry player is seeking a talented researcher with a Ph.D. in a quantitative field. This role offers the opportunity to work in a dynamic and innovative research-driven environment, utilizing advanced mathematical techniques and machine learning on extensive data sets. Successful candidates will demonstrate a strong track record of published research and the ability to contribute positively to model innovation. If you are self-motivated and eager to explore new research methodologies, this position is perfect for you, offering a highly competitive bonus structure for impactful contributions.

Benefits

Innovative research environment

Use of new research techniques

Highly competitive annual bonus

Qualifications

• Ph.D. in relevant fields with a track record of published research.
• Experience in trading or academic settings with data analysis.

Responsibilities

• Conduct innovative research using advanced mathematical techniques.
• Work with large data sets and apply machine learning methods.

Skills

C++

Machine Learning Techniques

Applied Mathematics

Self-motivated

Teamwork

Education

Ph.D. in Computer Science

Ph.D. in Econometrics

Ph.D. in Electronic Engineering

Ph.D. in Mathematics

Ph.D. in Physics

Ph.D. in Statistics

Job description

Qualifications

A Ph.D. in Computer Science, Econometrics, Electronic Engineering, Mathematics, Physics or Statistics. You will have a track record of published research work in respected journals. Applications from candidates who have completed a post-doctoral research position are particularly welcome.

Relevant Experience

Successful candidates will have substantial academic or trading experience in at least one of the following areas:

• Applied Mathematics such as Cryptography, Fluid Mechanics, and Optimisation.
• Linear and non-linear time series and spectral analysis (ARIMA, TAR, VAR, SSA etc.)
• Machine learning techniques such as DNN's, LSTM, LASSO, Random Forest, and XGBoost.
• Multivariate methods such as PCA and ICA, Factor Analysis, and Cluster Analysis.

Essential Skills:

• Experienced in C++ on very large data sets.
• Self-motivated with high curiosity.
• Ability to work independently and with a team.

Benefits

• Work alongside similar people in an innovative research-driven environment.
• Ability to use new research techniques on ever-growing data sets.
• Highly competitive annual bonus payments to successful candidates who demonstrate positive innovation in models and processes.