Assist in designing, coding, and maintaining tools for the systematic trading infrastructure of the team Build and maintain robust data pipelines and databases that ingest and transform large amounts of data Develop processes that validate the integrity of the data
Implementation and operation of systems to enable quantitative research (i.e. large scale computation and serialization frameworks)
Live operation of such systems, including monitoring and pro-active detection of potential problems and intervention
Stay current on state-of-the-art technologies and tools including technical libraries, computing environments and academic research Collaborate with the PM and the trading group in a transparent environment, engaging with the whole investment process Preferred Technical Skills Expert in Python and/or KDB/Q Proficient in modern data science tools stacks (Jupyter, pandas, numpy, sklearn) with machine learning experience Good understanding of using Slurm or similar parallel computing tools Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related STEM field from top ranked University Proficient in quantitative analysis, mathematical modelling, statistics, regression, and probability theory Proficient in professional software development methodologies, version control systems, unit testing and debugging tools, and micro-services architecture Excellent communication, problem-solving, and analytical skills, with the ability to quickly understand and apply complex concepts Preferred Experience 2+ years of experience in algorithmic trading systems development, preferably in systematic equity trading markets. Experience working with and centralizing multiple vendor data sets Experience collaborating effectively with cross functional teams, multitasking and adapting in a fast-paced environment Highly Valued Relevant Experience Entrepreneurial mindset Ability to multitask and adapt Curiosity and eagerness to learn and grow professionally Self-motivated, detail-oriented, and able to work independently in a fast-paced environment Target Start Date ASAP
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