Quantitative Research Analyst - Jersey City, NJ

SCM is committed to a workplace that values and promotes diversity, inclusion and equal employment opportunity by ensuring that all employees are valued, heard, engaged and involved at work and have full opportunities to collaborate, contribute and grow professionally. We are currently seeking a highly driven, well organized, and motivated candidate to join our team. Primary Responsibilities: Utilize your analytical and quantitative skills, market knowledge and intuition to develop and implement automated statistical trading models. Participate in all aspects of research and trading model development, including generating research ideas, building and analyzing data sets, conducting statistical data analysis and implementing quantitative production trading models. Requirements: A bachelors or advanced degree in a field providing a background in advanced statistical analysis of large data sets (includes, but is not limited to, economics, finance, statistics, mathematics or computer science). Programming experience, ideally including R, C++ and/or Python. Strong working knowledge of regression, time series analysis and other statistical techniques. Experience building, organizing and analyzing large data sets is preferred. The ability to comprehend and synthesize academic literature in finance, economics and statistics. Strong financial market interest. The ability to simplify and effectively communicate complex concepts. The base pay for this position is anticipated to be between $150,000 and $300,000 per year. The anticipated annual base pay range is current as of the time this job post was generated. This position is eligible for other forms of compensation and benefits, such as a bonus, health and dental plans and 401(k) contributions, which includes a discretionary profit sharing program. An employee's bonus and related compensation benefits can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.

Created: 2024-10-29