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Typical Set LLC – Berkeley, CA – seeks a Member of Research Staff 2 (multiple openings) to drive the development of predictive models and other components of the Company’s automated research and trading systems and software. Independently solve complex and ambiguous research problems and demonstrate good decision making to aid in the development, maintenance, and improvement of statistical models for generating large returns on financial market investments. Participate in and oversee maintenance and improvement of statistical models. Contribute to research project planning and draft Product Backlog Items. Apply modern statistical machine-learning methods to data analysis, false discovery control, model selection, stochastic optimization, large-scale inference, and online learning to high-dimensional data sets. Propose new approaches to problem solving in machine learning and create environments to test novel ideas or theories. Import/format/prepare large sets of data for processing through statistical and predictive models to identify relationships and trends. Utilize statistical programming languages such as R, Python, and SQL to work on numerical computation software for development and implementation of research and trading software. Independently write software that meets production standards and review code written by other researchers and software developers that goes into production. Analyze and fix production issues, including identifying and eliminating discrepancies between simulated and production trading, running production jobs with alternate data to avoid delays in production forecasting. Communicate with management, infrastructure, and trading staff to reach common project milestones. Build relationships with junior and senior internal and external personnel in own area of expertise. Participate in weekly research meetings and present statistical research results. Implement project-level solutions spanning longer time horizons and guide others on how to solve problems. Take on a more formal leadership role within the team by mentoring team members and giving clear direction and showing judgement in approach to meeting goals. Operate with little instruction on day-to-day work, requiring only general direction on new projects.

Requirements: Master’s degree or foreign equivalent in statistics, computer science, mathematics, engineering, operations research or a related field. Must have completed at least two (2) internships in a statistics, quantitative finance, machine learning, or related field; will also accept post-doctoral experience (no minimum required) in statistics, quantitative finance, machine learning, or related field. Must possess ability to (1) write production-quality software and (2) analyze complex datasets independently, as demonstrated through prior industry or academic experience. Must have conducted supervised research as part of a PhD program in statistics, computer science, mathematics, engineering, operations research, or a related field. Must have published work in a peer-reviewed publication in the field of statistics, computer science, mathematics, engineering, operations research or related field. Requires competence in solving large-scale computing problems, as evidenced by satisfactory completion of a performance problem given by the company. Hybrid office/remote work arrangement available. Employee must reside within normal commuting distance of the office. Send resume to and use Req ID 107893.