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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