Quantitative Researcher

Jane Street • New York, NY

20h

About The Position

We are looking for Quantitative Researchers to help us build models, strategies, and systems that price and trade financial instruments. You’ll work side by side with experienced researchers who are committed to teaching, guiding, and supporting our newest hires, learning how we think about experiment design, dataset generation, time series analysis, feature engineering, and model building for financial datasets.
At Jane Street, our researchers, engineers, and traders sit a few feet away from each other and work together to train models, architect systems, and run trading strategies. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing tens of thousands of high-end GPUs. Depending on the day, we might be diving deep into market data, tuning hyperparameters, debugging distributed training performance, or studying how our model likes to trade in production.
We don’t believe in “one-size-fits-all” modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. The most successful researchers will be driven by a curiosity for how their contributions fit into the larger picture of our trading operations, and how to adapt their findings into actionable strategies.

Requirements

• Able to apply logical and mathematical thinking to all kinds of problems
• Intellectually curious; eager to ask questions, admit mistakes, and learn new things
• A strong programmer who’s comfortable with Python
• An open-minded thinker and precise communicator who enjoys collaborating with colleagues from a wide range of backgrounds and areas of expertise

Nice To Haves

• Experience with data science or machine learning
• PhD or other research experience

Responsibilities

• Build models, strategies, and systems that price and trade financial instruments.
• Learn and apply experiment design, dataset generation, time series analysis, feature engineering, and model building for financial datasets.
• Train models, architect systems, and run trading strategies.
• Dive deep into market data, tune hyperparameters, debug distributed training performance, and study model performance in production.
• Apply statistical and ML techniques, from linear models to deep learning.
• Adapt findings into actionable strategies.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed