Our client is a process driven investment management group consisting of a team of researchers, traders and technologists who harness and apply the power of technology and automation to identify, model and trade global financial markets. They offer an array of quantitative investment fund products to their clients.

They are seeking candidates with exceptional academic credentials to join our team and participate in and support of the firm’s efforts in the research, trading and production processes.

They look for candidates who are eager to make an impact by doing real, hands-on research and development. Candidates must possess exceptional knowledge of mathematical and statistical methods as well as a proven ability to solve complex problems. A desire to work with large data sets and apply creative thinking is required. Successful candidates will also have deep interest in learning about trading and the financial markets.

What you can expect

They offer a supportive environment that fosters independent thought in a collegial, results oriented, work setting. Researchers and developers here are passionate about their work, model building, data and technology.

You are curious and intellectually driven to succeed. You'll be provided with the tools, resources and training required to satisfy that curiosity and passion, leading them to new insights and discoveries. The process driven approach enables these insights to be thoroughly tested in a systematic fashion and ultimately, if confirmed, integrated into our portfolio.

Role

Machine Learning Researchers help solve the unsolved. They use their knowledge in mathematics, optimization, and computer science to create new algorithms to solve previously unsolved problems.

What You Will Do

You will work to improve signal generation by upgrading the foundational algorithms. You will work closely with Data Scientists / Analysts and Machine Learning Engineers to optimally solve new problems.

Minimum Requirements

• Strong understanding of data structures and algorithms.
• Strong understanding statistical learning theory.
• Strong understanding of convex or non-convex optimization.
• Experience developing new machine learning algorithms.
• Ability to prove generalization guarantees for some commonly used algorithms.
  
  

• Production grade software engineering.
• Domain expertise.
• Published in top machine learning journals or conferences: JMLR, COLT, etc.