Trexquant actively trades multiple asset classes, including global equities, futures, corporate bonds, options, and foreign exchange. Data is at the core of everything we do and we are looking for individuals who are passionate about working with data and are curious about how it is transformed into robust and profitable predictive models. As a data scientist, you will specialize in one of these asset classes, becoming the go-to expert for all data-related matters within that domain. This is a unique opportunity to lead the data efforts for a specific asset class and make a direct impact on the firm's trading strategies.

Responsibilities

Develop deep familiarity with datasets in your asset class by engaging with data vendors and attending data conferences
Evaluate and explore new datasets recommended by researchers and partners
Collaborate closely with strategy and machine learning teams to identify predictive signals and help develop models using relevant data variables
Ingest, clean, and normalize large-scale datasets; build and maintain data pipelines and infrastructure for research and production
Stay up to date with advancements in data science and machine learning techniques relevant to quantitative investing

Requirements

2+ years of experience in data science, with a focus on quantitative analysis and model development

Bachelor's, Master's, or Ph.D. in Mathematics, Statistics, Computer Science, or a related STEM field
Strong quantitative and analytical skills, with a deep understanding of statistical modeling and data-driven problem solving
Proficient in Python, with experience using relevant libraries for data analysis, machine learning, and numerical computing

Benefits

Competitive salary plus bonus based on individual and company performance
Collaborative, casual, and friendly work environment
PPO Health, dental and vision insurance premiums fully covered for you and your. dependents
Pre-tax commuter benefits
Weekly company meals

Trexquant is an Equal Opportunity Employer

Trexquant Investment LP

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