Top Tech Jobs & Startup Jobs in NYC, NY

Manage US Card complaints governance processes, ensuring accurate documentation, operational efficiency, risk reduction, and compliance with enterprise complaint standards. Lead strategic initiatives, process improvements, transformation efforts, roadmaps, stakeholder communications, issue resolution, and audit or control activities. Collaborate across product, technology, operations, and channel teams to influence decisions, improve customer and agent experiences, and deliver timely outcomes.

Leads brand strategy and integrated, multi-channel marketing campaigns for luxury travel. Manages creative projects from briefing through execution, ensures brand consistency, partners with legal, compliance, research, creative, and business teams, and drives account growth, brand affinity, customer engagement, and campaign optimization.

Leads product management for enterprise tokenization solutions that protect sensitive data across third-party ecosystems. Partners with engineering, business lines, and external vendors to analyze data-sharing patterns, develop secure SDKs and APIs, enable integrations, and deliver scalable digital products. Drives customer-focused strategy, technical execution, risk mitigation, and cross-functional organizational alignment.

Own the Allspice platform ecosystem that delivers personalized credit card offers at scale. Lead product strategy, customer-focused digital experiences, AI-enabled capabilities, experimentation, and scalable system development. Partner with engineering, data science, marketing, and cross-functional teams to launch resilient features, solve complex problems, and drive business outcomes.

Leads software engineering projects and developers building distributed microservices and cloud-based solutions. Collaborates with product managers, mentors engineers, evaluates or writes code, and applies Java, Python, SQL, Node.js, Go, Scala, databases, Docker, Kubernetes, and cloud technologies including AWS. The role addresses regulatory needs and supports financial products serving millions of customers.

Lead a team of software developers and a portfolio of technology projects. Build distributed microservices and full-stack systems supporting regulatory needs and customer experiences. Develop cloud-based solutions using languages including JavaScript, Java, TypeScript, SQL, Python, and Go, along with AWS, databases, Docker, and Kubernetes. Mentor engineers, promote modern technology practices, collaborate with product managers, and contribute to Agile delivery and innovation.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Partner with data scientists, software engineers, ML engineers, and product managers to deliver scalable AI products. Research areas include deep learning, graph and sequential models, model optimization, self-supervised learning, robustness, explainability, RLHF, LLM fine-tuning, and data preparation. Own research agendas, publish innovative work, and translate technical findings into business outcomes.

Owns ML infrastructure and model delivery product roadmaps for Capital One’s Card Intelligence team. Defines customer-focused problems, prioritizes capabilities, establishes success metrics, partners with data science and technology teams, leads agile ceremonies, and drives scalable solutions aligned with business outcomes. Requires product management experience or a quantitative, business, or marketing degree, with preferred experience in data engineering, MLOps, machine learning infrastructure, or software engineering.

Supports multiple senior executives through complex calendar management, travel coordination, expense reporting, meeting and event planning, information dissemination, and team relationship building. Acts as an executive gatekeeper and team ambassador while solving problems independently, adapting to changing priorities, and maintaining high standards of organization, communication, and confidentiality.

Conduct model risk audits using econometric, statistical, and machine learning methods. Review enterprise models and risk practices, identify issues, develop analytical solutions, communicate findings to varied audiences, and maintain transparent documentation. Partner cross-functionally with business lines and build cloud-based, data-driven solutions. Required expertise includes regression, survival analysis, panel data, time-series analysis, machine learning, large datasets, and programming in R, Python, or SQL.