Top Tech Jobs & Startup Jobs in NYC, NY

Leads strategy and execution for identity journey intelligence data features and customer authentication data products. Partners with engineering, analysts, and business teams to prioritize feature catalogs, drive enterprise adoption, identify fraud and customer-experience use cases, and shape journey-level data-driven decisioning.

Build and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Own complex architectures, reliability SLOs, security and compliance reviews, technical roadmaps, and cross-functional delivery while mentoring engineering teams.

Leads product vision, strategy, and roadmap for authenticator and vendor speed-to-market capabilities. Drives initiatives across product development, vendor onboarding, and identity platform lifecycles, including self-service policy tools, AI automation toolkits, vendor process blueprints, automated testing, and standardized datasets. Partners cross-functionally to accelerate deployment of fraud-mitigation solutions while improving customer experience and secure access.

Lead data science for Capital One’s US Card organization by building and deploying machine learning models across the credit card lifecycle. Partner with software engineers and product managers, analyze large-scale customer data using Python, cloud, and distributed computing technologies, and translate statistical insights into business outcomes. The role also emphasizes innovation, stakeholder communication, technical leadership, talent development, and managing complex data science initiatives.

Lead technology projects and developers building cloud-based, full-stack microservices and risk management solutions. Design service boundaries, APIs, reusable frameworks, and distributed systems while establishing engineering standards and operational excellence. Collaborate with product managers and architects, improve system performance, reduce technical debt, evaluate emerging technologies, and mentor engineers. The role uses modern programming languages, databases, containers, cloud platforms, CI/CD, infrastructure as code, automated testing, observability, and AI-powered development tools.

Supports multiple executives by managing complex calendars, coordinating meetings and events, arranging travel, preparing expense reports, handling business communications, and completing administrative projects. The role requires strong organization, adaptability, problem-solving, writing, proofreading, relationship-building, and proficiency with office and video-conferencing technologies in a fast-paced environment.

Manage data analytics for PULSE Risk & Compliance by analyzing large datasets, identifying transaction anomalies and risk exposure, validating models, improving reporting and processes, and supporting compliant data strategies. The role involves Python-based programming, BI, database and data-storage design, strategic testing, cross-functional collaboration, project leadership, and people management in a hybrid environment.

Analyze complex data to support emerging payments and AirKey initiatives. Build data solutions, self-service tools, dashboards, metrics, and visualizations; provide business insights; and support data quality, metadata, lineage, security, and access governance. Collaborate with business and technology teams while using Python, R, Spark, SQL, Snowflake, AWS, and data governance tools.

Develop machine learning and statistical models across the full lifecycle, including design, training, evaluation, validation, and implementation. Analyze large-scale numeric and textual datasets using Python, cloud computing, and distributed data technologies. Collaborate with data scientists, software engineers, and product managers to translate complex analyses into customer-focused business outcomes. Apply methods including classification, clustering, sentiment analysis, time series, and deep learning.

Designs, builds, deploys, and maintains production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI, governance, and security practices. The role collaborates with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.