Jobs at BNY

Oversee pricing model monitoring and governance for FX, interest rate, and credit derivatives. Analyze P&L, valuations, risk, model performance, data quality, and control effectiveness. Prepare governance and regulatory materials, collaborate across markets and control functions, and apply data science to exception detection and automated workflows. Support semantic data initiatives and deliver insights that improve model oversight and operational decision-making.

Lead SOC alert triage, incident investigations, root cause analysis, containment, eradication, and recovery. Correlate SIEM, EDR, network, cloud, and identity telemetry; maintain detections and playbooks; conduct threat hunting; and coordinate incident response. Mentor analysts, support purple-team exercises, produce executive incident reporting, improve controls, and maintain audit-ready documentation. The role requires enterprise SOC experience, scripting, security certifications or equivalent expertise, and shift, weekend, and holiday flexibility.

Lead SOC alert triage, incident investigation, containment, recovery, threat hunting, and root cause analysis. Maintain detection logic, playbooks, and audit-ready documentation across SIEM, EDR, cloud, identity, and network platforms. Mentor junior analysts, coordinate with threat intelligence and engineering teams, support purple team exercises, produce executive reports, and improve security operations across a large enterprise SOC while working rotating shifts and occasional weekend or holiday coverage.

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.

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.

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.