Quantitative Researcher - Experienced Hires (CHINA)
At a glance
Mid-level Data Science role at Trexquant Investment. Beijing · full-time.
Pay not stated Data Science salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and tune machine-learning models that forecast liquid assets, drawing on financial data and trading signals
- Work through large datasets to find alpha signals and turn them into systematic trading strategies
- Test quantitative-finance research and use simulations to evaluate trading ideas with other researchers
- Improve existing models by adding data sources and methods, with an eye to performance and scale
What you bring
- A bachelor's, master's or doctoral degree in a STEM discipline
- At least three years in systematic trading, ideally with evidence of live trading
- Fluency in Python or another programming language, plus a strong interest in machine learning
- Strong English for everyday work discussions
Who this fits
This suits a researcher who can work independently and collaborate on experiments and strategy development. The role is based on-site in Beijing.
From the employer
Trexquant is a systematic hedge fund where we use thousands of statistical algorithms to trade equity, futures and other markets globally. Starting with many data sets, we develop large sets of features and use various machine learning methods to discover trading signals and effectively combine them into market-neutral portfolios. We are looking for data scientists, physicists, engineers, economists and programmers to develop the next generation of machine learning strategies that can accurately predict the future movements of liquid financial assets.
As a Quantitative Researcher you will be involved in developing market-neutral signals, parsing and analyzing large data sets and collaborating with the Data and Strategy Research team to build a diverse set of predictive models. While we are open to researchers in any asset class we are currently focusing on roles in equities, futures, commodities, and event driven research.
Responsibilities
- Design, implement, and optimize various machine learning models aimed at predicting liquid assets using a wide set of financial data and a vast library of trading signals.
- Parse and analyze large datasets to identify actionable alpha signals and develop strategies for systematic trading.
- Investigate and implement state-of-the-art academic research in the field of quantitative finance.
- Continuously innovate and improve existing models by integrating new data sources and advanced techniques to boost performance and scalability.
- Collaborate closely with a team of experienced quantitative researchers to conduct experiments, backtest hypotheses, and refine strategies through rigorous simulations and data analysis.
Requirements
- BS/MS/PhD degree in any STEM field
- 3+ years in a systematic trading environment, preferably with a live trading track record
- Strong interest for machine learning
- Fluent with programming languages such as Python
- Strong problem-solving skills
- Ability to work effectively both as an individual and a team player
- Strong English communication skills for daily professional interactions.
Benefits
- Competitive compensation with bonus tied to the performance of algorithms you develop
- Work in a collaborative and friendly environment, participate in decision-making process for research direction, and have opportunity to lead on new ideas
- Comprehensive benefits including healthcare and insurance
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