Role at a glance
- Job function
-
Investments & Capital Markets Portfolio Management & Trading
- Salary
- Not Disclosed
- Location
- Hong Kong
- Work arrangement
- On-site
- Employment
- Full-time
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Role Summary
Quantitative Researchers build models, strategies, and systems to price and trade financial instruments. They apply statistical and machine-learning techniques to financial datasets and work with colleagues to refine their approaches into actionable trading strategies.
What You'll Do
- Apply experiment design, dataset generation, time series analysis, feature engineering, and model building to financial datasets.
- Develop and refine models, strategies, and systems for pricing and trading financial instruments.
- Analyze market data, tune model hyperparameters, and debug distributed training performance.
- Study how models behave in production trading.
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View full postingQualifications
Able to apply logical and mathematical thinking; intellectually curious and eager to learn; strong programmer comfortable with Python; open-minded, precise communicator who enjoys collaboration; fluent in English.
Required
- Logical and mathematical thinking
- Intellectual curiosity and eagerness to ask questions, admit mistakes, and learn
- Strong programming skills and comfort with Python
- Open-minded thinking, precise communication, and enjoyment of collaboration
- Fluent in English
Original job description
Content provided by the employer
Original job description
Content provided by the employer
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 apply your experience in experiment design, dataset generation, time series analysis, feature engineering and model building to financial datasets, while collaborating with colleagues who will challenge and refine your approach.
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 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” modelling solutions; we are open to and excited about applying all different types of statistical and machine learning 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.
About You
If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. If you have a curious mind and a passion for solving interesting problems, we have a feeling you’ll fit right in. You should be:
- 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
- Fluent in English
If you're a recruiting agency and want to partner with us, please reach out to [email protected].
About the company
Jane Street
Large Enterprise
Jane Street is a global trading firm that specializes in quantitative and algorithmic trading across various asset classes, including equities, options, and fixed income. Founded in 2000, the company leverages advanced technology and data analytics to drive its trading strategies while maintaining a strong commitment to risk management and collaboration. Known for its dynamic and intellectually stimulating work environment, Jane Street also places a high value on continuous learning and professional development, making it an attractive workplace for individuals interested in finance, mathematics, and computer science.