Top Senior Level Analytics Jobs

The Trade Analyst - Power Lead will provide day-to-day support to Energy trading and origination teams, focusing on PnL analysis, risk reporting, and complex trade support. Responsibilities include mentoring junior analysts, collaborating with various departments for problem resolution, and ensuring efficient onboarding of new desks and traders. The role requires leveraging proprietary systems for accurate data management and reporting.

The Principal Data Management role involves designing and implementing a long-term enterprise data management and governance program. Responsibilities include developing data quality applications, managing relationships with federated data governance groups, and partnering with external vendors to enhance the program's maturity.

The HRIS Senior Analyst at Chainlink Labs will build strong relationships with stakeholders, provide production support, perform configuration and administration of Workday modules, assess new features, ensure data integrity, document processes, and promote best practices for quality assurance.

The Cost Analyst will evaluate product designs, develop cost estimates, review vendor quotes, and analyze cost breakdowns for material and labor. The role involves managing cost budgets, guiding teams on margin expectations, mentoring staff, and keeping management informed of significant issues.

The LME Legal Analyst will review leveraged finance and high yield documentation, produce analyses on distressed scenarios, answer client queries about covenant packages, and use proprietary technology to enhance restructuring databases. The role involves delivering seminars and representing 9fin at industry events.

The Senior Research Data Steward manages data and metadata for defined domains, ensuring data integrity and compliance. Responsibilities include collaborating with data owners, establishing data management plans, and promoting best practices in data governance. The role involves communicating with various stakeholders and providing training to enhance data management processes.

Develop and implement custom GenAI models for various supply chain challenges, manage large datasets, train and evaluate models, deploy models into existing systems, collaborate with cross-functional teams, stay updated on GenAI technologies, and contribute to research and development.