Quantitative Researcher - Machine Learning

Risk Hub; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Point72's Internal Alpha Capture (IAC) team is seeking exceptional machine learning researchers to join our collaborative, fast-growing group focused on AI-driven equity trading signals drawn from proprietary datasets and state-of-the-art ML methods. You will work with experienced researchers to ideate, implement, evaluate, and apply novel models, adapting existing approaches to improve portfolio signals.

Prior finance experience is not required, but strong communication and teamwork are

Qualifications

• Master’s or PhD in machine learning, computer science, statistics, or related fields.
• Strong proficiency in ML libraries such as Torch, JAX or TensorFlow.
• Experience applying ML to large, possibly noisy datasets.

Responsibilities

• 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.

Skills

Machine learning research

Python

Communication

Software engineering principles

Collaboration

Education

Master’s or PhD in ML/CS/Statistics or related fields

Tools

Torch

JAX

TensorFlow

Job description

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.