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Position Summary DUTIES:

Develop a deep understanding of Two Sigma's systematic modeling and trading platforms and the global equity and derivatives markets they operate in. Monitor daily trading strategy behavior and proactively identify issues that may present risks to Two Sigma's investors and portfolios, including participating in the team's Risk Support Rotation responsible for escalating issues in real-time to trading businesses, portfolio managers, and engineering and modeling partners. Lead root-cause investigations into trading strategy behavior anomalies and operational issues, coordinating with engineering, modeling, and trading partners to resolve them. Build and use tools for quantitative trading strategy analysis using Python. Perform statistical analysis including regression, correlation, and other quantitative methods to evaluate systematic strategy behavior and risk exposures. Prepare, review, and present reports on systematic strategy behavior and risk to stakeholders including portfolio managers and investor-facing teams. Gather requirements from multiple partner teams and design, implement, and test new metrics, dashboards, and analytical tools for the Risk department. Develop and maintain automated alerts, sanity checks, and reporting systems for the Risk department and its partners. Maintain and enhance key data profiling and alert generation platforms, working with large codebases and implementing unit and integration testing to ensure code reliability. Review and trace through code to troubleshoot issues and implement test cases for code validation. Develop scripts and automated workflows for strategy access control and operational risk processes, working within a Linux environment using command-line tools for file operations, process management, and data processing. Perform, document, and improve operational Risk processes, with an eye towards automation, including writing clear technical documentation, runbooks, and process guides. Collaborate regularly with modelers, portfolio managers, traders, and investor-facing teams on cross-functional initiatives to improve risk management capabilities.

Minimum Requirements:

Bachelor's degree or the equivalent in Computer Science, Applied Mathematics, Computational Finance and Risk Management, or related field plus 1 year of experience in finance, operational risk management, software engineering or related experience. Must have experience with developing, maintaining & troubleshooting codebases, including code review & implementing test cases for code validation. Must have experience with programming in Python, including numerical & data analysis libraries (Pandas, Numpy) for quantitative analysis. Must have experience with querying, manipulating & managing data using database tools, including processing & profiling large datasets from multiple sources, data cleaning, quality checking & validation. Must have experience with applying statistical analysis methods including regression, correlation & other quantitative methods for evaluating strategy behavior & operational risks. Must have experience with operating in Linux environments, including basic commands for file operations, process management, text search & data processing. Must have experience with designing & building automated tools, alerts & analytical reporting for data-driven decision-making. Must have experience with monitoring operational processes & system outputs, identifying & escalating anomalies or issues. Must have experience with preparing technical documentation, research reports & analytical findings & collaborating with cross-functional teams including investment, engineering & operations stakeholders. Must have experience with conducting root-cause investigations of operational issues. Must pass company's required skills assessment. Employer will accept any amount of experience with the required skills.

Rate of pay:

The base pay for this role will be between $150,000 and $200,000 per year. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus 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. #LI-DNI