Jobs at BNY

Monitors data feeds, performs data scrubbing, identifies exceptions, maintains and verifies financial data, and supports database and spreadsheet analysis. The role tests software enhancements, prepares basic reports and charts, responds to data-related client questions, and guides junior staff. Candidates need a bachelor’s degree, 4–5 years of investment banking experience, capital markets knowledge, and intermediate Excel proficiency. Experience with security master, security valuation, fund accounting, trade capture, or securities settlement is preferred.

Lead production and application support for business-critical banking and financial services applications. Manage incidents, problems, changes, service requests, root-cause analysis, remediation, monitoring, stakeholder communication, SLAs, regulatory compliance, and operational risk. Support Unix/Linux environments, Informatica ETL workflows, file transfers, batch processing, scheduling, data integration platforms, Python automation, and Snowflake administration in a customer-facing production environment.

Leads production application support for critical enterprise business applications, ensuring stability, availability, and reliability. Responsibilities include proactive monitoring, incident, problem, and change management, root cause analysis, issue remediation, stakeholder coordination, operational risk mitigation, service improvement, automation, and process optimization. The role requires Unix/Linux troubleshooting, Informatica ETL workflow monitoring, production support, file transfer, batch processing, scheduling, and data integration expertise.

Monitors and reports on client portfolio performance, maintains online reporting platforms, analyzes account activity and investment practices, and answers client inquiries about products, risk, and asset allocation. Develops market and industry research, identifies product and revenue opportunities, trains clients on report generation, recommends service improvements, and provides guidance to less experienced staff.

Leads data management and quantitative analysis strategy, ensuring data integrity, accuracy, and governance across platforms. Translates complex data insights into business strategies, drives process automation and continuous improvement, and collaborates cross-functionally to support data-driven decisions. The role also mentors team members, promotes innovation, and advances data management practices through emerging technologies and industry trends.

Design and maintain backend services and APIs, enhance performance, implement testing, and collaborate with cross-functional teams while fostering a learning culture.

Lead design, implementation, and delivery of enterprise network security (firewalls, proxy, WAF) across datacenter, DMZ, cloud and endpoints. Own vulnerability management, observability, logging/SIEM, certificate governance, automation (Python/Ansible), and compliance aligned to ITIL and regulatory requirements. Provide technical leadership for modernization projects, stakeholder engagement, runbooks, and continuous control improvement.

Lead delivery and production support of reusable AI capabilities across infrastructure teams. Prioritize AI demand, coordinate cross-functional delivery, track KPIs/risks, enforce governance and controls, drive adoption and change management, and implement Run-the-Bank projects for Eliza/AI hub solutions on GCP.

Lead product vision, strategy, and development for shared platform services (reporting, access, authentication). Prioritize backlogs, define requirements, drive roll-outs, ensure adoption against OKRs, and coach product owners to improve product management practices across pods.

The Senior Vice President of Product Management leads product development and strategy, collaborates with teams, and drives key product initiatives while managing complex products.

Lead data science initiatives using advanced statistical techniques and machine learning to produce actionable insights. Partner with cross-functional teams to design data-driven solutions, manage and ensure integrity of large datasets, develop predictive models to forecast trends, and communicate results to stakeholders while staying current on emerging technologies.