Quantitative Researcher - Machine Learning

Point72; New York (NY); On-site; USD 150,000 - 200,000; Full time

Job summary

Point72 is seeking a Quantitative Researcher - Machine Learning for roles in New York, London, or Hong Kong. The role focuses on developing AI-driven equity signals, leveraging large datasets, and state-of-the-art ML methods. Prior finance experience not required, but strong research credentials are essential.

The team emphasizes collaboration, rigorous evaluation, and scalable experimentation in a fast-growing environment. Proficiency in ML libraries and Python is required.

Qualifications

• Master’s or PhD in machine learning, CS, statistics, or related field.
• Strong experience or exposure to modern ML models and large datasets.
• Proficiency in ML libraries (Torch, JAX, TensorFlow) and Python tooling.

Responsibilities

• Manage all aspects of the research process from ideation to evaluation and application.
• Identify, adapt, and extend ML models to develop new signals that enhance portfolio returns.
• Stay up to date on AI/ML advances and recommend new models/tools to seize opportunities.

Skills

ML research

Python proficiency

Analytical skills

Communication skills

Education

Master’s or PhD in ML/CS/Stats

Tools

Torch

JAX

TensorFlow

Python ecosystem & clustering

Job description

Quantitative Researcher - Machine Learning

New York, London, or Hong Kong

JOB RESPONSIBILITIES:

A highly collaborative, fast-growing team at Internal Alpha Capture (IAC), Point72 is developing AI-driven equity trading signals that leverage rigorous research, state-of-the-art machine learning methods, proprietary data sources, and unparalleled computing power.

We are looking for exceptional machine learning researchers to join our efforts. Researchers will work closely with our experienced team members and apply the full breadth of their machine learning knowledge to unique, proprietary datasets, and develop novel trading signals that have high impact. Prior experience in the financial industry is not required.

Key responsibilities may include:

• Managing all aspects of the research process, including ideation, method selection, implementation, evaluation, and eventual application.
• Identifying, adapting, and extending existing models in the broad field of machine learning; conducting novel research as needed, to develop new signals that can enhance portfolio returns, or predict other variables of interests.
• Staying up to date on the advances in AI/ML and related technological innovations to provide recommendations on new models and tools and identify emerging opportunities.

DESIRABLE CANDIDATES:

• Master’s or PhD in machine learning, computer science, statistics, or related fields.
• Knowledge and experience in any of the following areas are strongly preferred: modern sequence models, graph neutral nets, reinforcement learning, LLMs.
• Prior research experience utilizing machine learning over large, possibly noisy, data sets.
• Strong analytical and quantitative skills, and a detail-oriented mindset.
• Strong proficiency in machine learning libraries such as Torch, JAX or TensorFlow.
• Competence in Python, cluster environment, and general software engineering principles (source control, testing, collaborative workflow).
• Excellent written and verbal communication skills, willing to proactively engage other team members in helping to foster a highly collaborative, team-oriented research environment.
• Commitment to the highest ethical standards.

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