Quantitative Researcher - Machine Learning
At a glance
Mid-level AI Research role at Point72. New York · full-time · no salary stated · we estimate $181,850–$300,000.
Point72 has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and test trading models with machine learning, using a scientific approach to study market behavior
- Take research from raw data through cleaning, analysis, feature design, model implementation and performance review
- Choose relevant datasets for financial questions, then assess how well features support predictions
- Develop and prototype models that address complex financial problems
What you bring
- You are pursuing or hold a doctorate in machine learning, computer science, statistics or a related discipline
- You have used deep learning for sequential models and time-series forecasting, and worked with deep networks and representation learning
- You can turn mathematical methods into code and have research experience in a data-driven setting
- You are proficient in Python or R and have used TensorFlow or PyTorch
Who this fits
This role suits a researcher who can work independently while collaborating with others, and who is interested in applying machine learning to finance. Natural language processing experience is an advantage.
From the employer
About Cubist
Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.
Role/Responsibilities:
We are seeking a quantitative researcher for the Cubist Machine Learning Research group with experience in machine learning, especially recent deep learning and natural language processing technology.
Researchers will use a rigorous scientific method to develop sophisticated trading models and shape our insights into how the markets will behave. Successful researchers manage all aspects of the research process including data ingestion and processing, data analysis, methodology selection, implementation and testing, prototyping, and performance evaluation.
Researchers will be introduced to industry standard datasets, including understanding which data may be relevant to a certain model or financial problem; how to collect, parse, and clean the data; how to incorporate the data into innovative functional models; how to construct and develop features from raw data; and how to estimate effectiveness of such features.
Researchers will also be provided with the opportunity to implement the full breadth of their knowledge and training to actively participate in all stages of research & development of financial models through use of machine learning. Based on experience from working with existing industry-standard models and algorithms, researchers will learn how to construct their own models in order to solve complex financial problems and enhance data prediction capabilities within the financial services industry.
Requirements:
- PhD or PhD candidate in machine learning, computer science, statistics, or a related field
- Experience with sequential modeling and time series forecasting using deep learning
- Experience with deep neural networks and representation learning
- Prior experience working in a data driven research environment
- Experience with translating mathematical models and algorithms into code
- Proficient in programming languages such as Python and R
- Experience with machine learning software libraries such as TensorFlow or PyTorch
- Experience with natural language processing technology a strong plus
- Excellent analytical skills, with strong attention to detail
- Interest in applying machine learning to finance
- Collaborative mindset with strong independent research ability
- Strong written and verbal communication skills
We’re looking for exceptional colleagues with unparalleled passion. If you’d like your resume to stand out, tell us about your exceptional personal achievements, even if they have nothing to do with finance. Of course we love to hear more about specific engineering or data projects that you’ve worked outside of school, or as part of your curriculum. If you’re proud of the work you did we want to hear about it. In addition to exceptional statisticians and engineers, we work with talented musicians, writers, mathematicians, and founders of non-profits; we’d love to learn more about what excites you.