# Quantitative Researcher - Machine Learning at Point72

> United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/greenhouse%3Apoint72%3A7297513002 (read 2026-11-07).

## Facts (observed)

- Company: [Point72](https://alderhq.app/companies/point72)
- Location text: New York; New York, NY
- Where it can be done from (inferred): United States (evidence: "Location: New York; New York, NY")
- Posted: 2024-08-15 (813 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://boards.greenhouse.io/point72/jobs/7297513002?gh_jid=7297513002
- Skills listed (inferred): Python, PyTorch, TensorFlow

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (New York, n 90): p25 $177k, median $233k, p75 $275k
- H-1B filings at this company for similar roles, FY26: 5, median offered wage $300k ([US DOL OFLC LCA disclosure data](https://www.dol.gov/agencies/eta/foreign-labor/performance))

## Description (the employer's text)

### 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.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Apoint72%3A7297513002 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.