Statistical Arbitrage Researcher
Confirmed live in the last 24 hours
Jane Street

1,001-5,000 employees

Global liquidity provider and market maker using technology and
Company Overview
Jane Street is a global market maker and liquidity provider, leveraging advanced technology and quantitative analysis to trade on over 200 venues in 45 countries. The company fosters a collaborative culture, emphasizing teamwork, continuous learning, and intellectual exchange, with a focus on hiring humble, kind individuals who prioritize cooperation. With a blend of machine learning, distributed systems, and statistics, Jane Street's technology teams build robust systems that handle billions in transactions daily, making it a leader in efficient and transparent financial market operations.
Quantitative Finance
AI & Machine Learning
Data & Analytics
Financial Services

Company Stage

N/A

Total Funding

$2.5B

Founded

2000

Headquarters

New York, New York

Growth & Insights
Headcount

6 month growth

11%

1 year growth

27%

2 year growth

39%
Locations
New York, NY, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Python
CategoriesNew
Risk Management
Sales & Trading
Finance & Banking
Requirements
  • 2-6 years of professional experience working in a data-rich environment in quantitative research
  • Team player with a highly collaborative mindset
  • Open to a variety of techniques and modes of thinking
  • Humble about what you do and don't know; willing to admit mistakes
  • Enjoys learning new skills and teaching others what you know
  • Able to write code and analyze large datasets
  • Experienced with statistical and ML modeling
  • Knowledge of Python preferred, but not required
  • Background knowledge of financial markets is a plus
Responsibilities
  • Apply rigorous math and statistical methods to analyze a variety of input datasets
  • Create novel alpha-focused trading strategies for Jane Street
  • Assess quality and consider outliers, dimensionality, feature engineering, causality, aligning dates across datasets, and more
  • Stay vigilant in efforts to find and correct errors or mistakes in code
  • Collaborate and communicate fluidly with team members
  • Balance expertise and intellectual rigor with an open mind to a variety of techniques and modes of thinking