Quantitative Researcher - Machine Learning

Point72; New York (NY); On-site; USD 200,000 - 300,000; Full time

Job summary

An established industry player is seeking a Quantitative Researcher with expertise in machine learning and deep learning. This role involves developing advanced trading models and utilizing cutting-edge data analysis techniques to predict market behavior. The successful candidate will engage in a rigorous research process, leveraging industry-standard datasets and innovative methodologies. This position offers the chance to work collaboratively in a dynamic environment, where your contributions can significantly impact financial strategies. If you're passionate about applying machine learning to finance, this is a unique opportunity to advance your career.

Qualifications

• PhD or PhD candidate in machine learning, computer science, or statistics.
• Experience with deep neural networks and time series forecasting.

Responsibilities

• Develop sophisticated trading models using machine learning.
• Manage data ingestion, analysis, and model testing.

Skills

Machine Learning

Deep Learning

Natural Language Processing

Python

R

Analytical Skills

Attention to Detail

Collaboration

Education

PhD in Machine Learning

PhD candidate in Statistics

Tools

TensorFlow

PyTorch

Job description

Quantitative Researcher - Machine Learning

Join to apply for the Quantitative Researcher - Machine Learning role at Point72

Quantitative Researcher - Machine Learning

Join to apply for the Quantitative Researcher - Machine Learning role at Point72

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.

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
  
  

The annual base salary range for this role is $200,000-$300,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.

Seniority level

• Seniority level

  Internship

Employment type

• Employment type

  Full-time

Job function

• Job function

  Finance and Sales

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