RESPONSIBILITIES:
Apply strong data modelling and statistical skills to develop and implement data-driven systematic trading strategies.
Manipulate and analyze large datasets, including numerical and categorical data, using
appropriate techniques and tools.
Use creative and flexible problem-solving techniques to effectively curate and analyse proprietary sentiment data.
Assess and implement situation-appropriate statistical or machine learning methods to gain insight into data, and develop pattern recognition, anomaly detection and optimization methods relevant to the data at hand.
REQUIRED QUALIFICATIONS:
Experience trading complex financial products.
Solid understanding of capital markets concepts and quantitative financial methods.
Solid knowledge of probability and statistics to effectively analyze and interpret data.
Interest in game theory, decision theory, behavioral finance and strategy games.
Bachelors/Master’s Degree in Mathematics, Engineering, Physics or related field; Knowledge of applied mathematics, including convex optimization, quadratic programming, and partial differential equations.
Proficiency in Python.
Strong understanding of SQL for data manipulation and extraction.
Ability to integrate both classical statistical and machine learning methods into analysis techniques and methods.
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