We are currently partnered with several elite funds seeking those with strong machine learning backgrounds for positions as ML Quantitative Researchers. These funds encourage teamwork and academic brilliance and are at the very top echelon of finance. They have begun implementing ML and AI to expertly analyze alternative datasets and have found great success in doing so. They each have a sizable pool of resources to make almost any promising and relevant project a reality. These roles are based across the US.
If you are strong in machine learning and have a good basis at PhD level in Statistics, Physics and/or Mathematics, these positions will ensure early entry into what is very likely the future of the financial world.
The roles on a day-to-day basis involve:
- conducting quantitative research as part of a team
- combining financial insights and machine learning to analyze and explore large datasets
- using cutting-edge technology in tools, technical libraries, and computing
- continuously improving the entirety of the investment process
The requirements for these positions are:
- A PhD degree in Computer Science/Engineering, Physics (or Geophysics/Astrophysics), or Statistics with specialization in Machine Learning
- Strong experience in Python (and associated ML libraries such as scikit-learn, TensorFlow, PyTorch, etc.)
- Demonstrable and rich ML experience, including at least some advanced areas (e.g., reinforcement learning, deep learning) and all classical areas
Preferred experience:
- C++, R and/or Linux experience, and any relevant additional languages
- Experience working a distributed computing environment
- Strong academic background with publications in top machine learning/statistics journal/conferences
- Knowledge in numerical optimization and scientific computation
- Experience working independently as well as collaboratively in a fast-paced environment