Top Analysis Reporting Jobs in Buffalo, NY

Lead and manage projects building modern data ecosystems and managed data services. Oversee strategic planning, mentor teams, ensure project delivery and quality, design data strategies, and convert analytics into business opportunities.

This role oversees a team that analyzes MetLife's mortgage portfolios, delivers high-impact analytics, and collaborates with various stakeholders for reporting and compliance.

Support merchant onboarding, fraud detection, and risk analysis through data analysis and modeling. Collaborate cross-functionally to develop scalable monitoring frameworks.

The Analytics Manager for Credit Risk will lead credit strategy, enhance underwriting with machine learning, and manage cross-functional partnerships, focusing on data analytics and risk knowledge.

The Financial Systems Analyst manages NetSuite, optimizes business system performance, supports users, ensures data integrity, and collaborates on process improvements.

Lead analytics strategy for a financial institution, ensuring compliance and driving marketing analytics through predictive modeling and team management.

The Senior Data Analyst will analyze data, build models, and provide insights to support decision-making for operational and leadership teams, while collaborating with technical and non-technical stakeholders.

Lead Toast's credit risk strategy, overseeing portfolio management, product growth, and analytics while ensuring compliance with regulations and mentoring analysts.

Manage analytical service operations by overseeing a team, designing software for data mining, developing statistical models, and generating insights for marketing outcomes. Provide training and mentorship to direct reports.

Lead data governance for assigned domains: implement policies, monitor and remediate data quality, perform profiling and lineage documentation, track data quality KPIs, partner with stakeholders to embed standards, and support MDM and governance training.

Lead the ML Data Enablement team to define strategy and execution for accelerating third-party and internal data onboarding, enforce data quality and SLAs, build metadata and lineage standards, and deliver scalable data infrastructure and workflows to reduce time-to-production for ML models.