Quantitative Researcher

Jane Street Jane Street · Quant · Hong Kong · Quantitative Research

Quantitative Researcher role at Jane Street focusing on identifying market signals, analyzing data, building and testing models, and creating trading strategies. The role involves applying various statistical and ML techniques, including deep learning, to financial datasets. Responsibilities include experiment design, dataset generation, time series analysis, feature engineering, and model building. The role emphasizes learning and collaboration within a research-heavy environment.

What you'd actually do

  1. learn how we identify market signals, analyze large datasets, build and test models, and create new trading strategies
  2. working closely with full-time researchers on projects drawn from their own work
  3. gain a better understanding of the diverse array of challenges we consider every day, learning how we think about experiment design, dataset generation, time series analysis, feature engineering, and model building for financial datasets
  4. Your day-to-day project work will be complemented by classes on the broader fundamentals of markets and trading, lunch seminars, and activities designed to help you understand the entire process of creating a new trading strategy, from initial exploration to finding and productionizing a signal.

Skills

Required

  • Strong programmer comfortable with Python
  • Fluent in English

Nice to have

  • experience with data science or machine learning
  • Research experience
  • Able to apply logical and mathematical thinking to all kinds of problems
  • Intellectually curious
  • An open-minded thinker and precise communicator who enjoys collaborating with colleagues from a wide range of backgrounds and areas of expertise

What the JD emphasized

  • applying all different types of statistical and ML techniques
  • deep learning
  • experiment design
  • dataset generation
  • time series analysis
  • feature engineering
  • model building for financial datasets

Other signals

  • applying all different types of statistical and ML techniques
  • deep learning
  • experiment design
  • dataset generation
  • time series analysis
  • feature engineering
  • model building for financial datasets