# Quantitative Research Intern at Point72

> United States · Intern · Other · open. Source: https://alderhq.app/jobs/greenhouse%3Apoint72%3A7297667002 (read 2026-11-06).

## Facts (observed)

- Company: [Point72](https://alderhq.app/companies/point72)
- Location text: New York, Seattle; New York, NY
- Where it can be done from (inferred): United States (evidence: "Location: New York, Seattle; New York, NY")
- Posted: 2024-08-15 (812 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/7297667002?gh_jid=7297667002
- Skills listed (inferred): Python, Java, C++, C#/.NET

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (New York, n 114): p25 $42k, median $66k, p75 $89k
- H-1B filings at this company for similar roles, FY26: 11, median offered wage $300k ([US DOL OFLC LCA disclosure data](https://www.dol.gov/agencies/eta/foreign-labor/performance))

## Description (the employer's text)

### JOB DESCRIPTION

This is an opportunity for students and researchers of advanced data modeling and statistical learning methods to apply these techniques to market prediction and systematic trading.

- Pre-process (validate, clean, normalize, reduce dimension) very large data sets for model estimation and event studies
- Identify features and relationships useful for the predictive modeling of market dynamics
### DESIRABLE CANDIDATES

- MS, or PhD candidates in finance, computer science, mathematics, physics, or other quantitative discipline
- Programming in any of the following: C++, Java, C#, MATLAB, R, Python, or Perl
- Strong analytical and quantitative skills
- Demonstrated interest in financial markets and systematic trading
- Clear, concise, and proactive communicator
- Detail-oriented
- Willing to take ownership of his/her work, working both independently and within a small team

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Apoint72%3A7297667002 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.