Capital One

Account Executive

• Lead the regional operational delivery of the Technical Quality of Acceptance strategy across Latin America, the Caribbean and Mexico
• Lead, mentor and develop a high-performing team of operational professionals, including managers and specialists across country acceptance, analytics, PSP-related acceptance and referral investigation
• Set clear goals, manage performance and capacity, and build technical and operational capability
• Supervise end-to-end diagnosis and remediation of acceptance breakages across merchants, POS, ecommerce or ATM environments, gateways or orchestrators, PSPs, processors and acquirers
• Coordinate specialist resources and apply problem-management practices, including root-cause analysis, remediation ownership, progress tracking, recovery validation and lessons learned
• Lead complex cross-functional regional initiatives to strengthen technical acceptance resilience, improve monitoring and detection, remove recurring failure modes and support technical acceptance stability
• Apply project-management practices covering scope, milestones, dependencies, risks, governance, stakeholder communications and delivery plans
• Build partnerships across Networks, Operations, Sales, Market Development and Strategy teams; influence decisions and drive alignment
• Establish and maintain KPIs, reporting, playbooks, escalation paths, operating controls and documentation
• Represent the regional Technical Quality of Acceptance function in governance forums, launch-readiness reviews, roadmap discussions and executive updates
• Develop team and stakeholder capability through training and knowledge sharing
• Engage externally when necessary to clarify technical issues, coordinate remediation or support strategic acceptance priorities
• Approximately 15% travel

Bilingual (Spanish) Senior Manager, Account Executive Operations

Machine Learning Engineer

• Design, build, and/or deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams
• Inform ML infrastructure decisions using knowledge of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
• Write and test application code, develop and validate ML models, and automate tests and deployment
• Collaborate on cross-functional Agile teams to create and enhance software enabling big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines feeding ML models
• Apply continuous integration and continuous deployment practices, including test automation and monitoring
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and apply Responsible and Explainable AI best practices
• Use programming languages including Python, Scala, or Java

Machine Learning Engineer 4 – Intelligent Foundations and Experiences

Data Analyst

• Drive strategy and implementation of AI-readiness initiatives for the Discover Personal Loans business
• Centralize, document, and optimize data for advanced analytical modeling
• Modernize monitoring and governance processes by automating manual controls and establishing scalable data abstraction layers
• Champion data quality and standardization efforts for core business data
• Leverage analytics to improve customer experience
• Lead, mentor, and develop a team of junior analysts
• Participate in the full life cycle of projects
• Collaborate across business units
• Mine data for crucial organizational information
• Implement process, data, and reporting improvements
• Work with large and complex databases containing millions to billions of records
• Improve operational efficiency and effectiveness
• Conduct analyses to evaluate processes and tests
• Extract and analyze data to assess product offerings
• Consult on new production and data storage systems
• Develop and implement strategic tests for products, initiatives, and marketing materials

Manager, Data Analysis – Personal Loans

AI Engineer

• Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products
• Design, develop, test, deploy, and support AI software components, including foundation model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability
• Leverage open-source and SaaS AI technologies such as AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch
• Invent and introduce state-of-the-art foundation model optimization techniques to improve scalability, cost, latency, and throughput
• Contribute to the technical vision and long-term roadmap of foundational AI systems
• Own end-to-end architecture for complex AI systems, ensuring maintainability, observability, and ethical alignment
• Define and maintain service-level objectives for AI reliability, including latency, uptime, and model performance drift
• Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate model inference pipelines
• Lead cross-functional technical reviews for AI system deployments, ensuring security, data governance, and compliance standards
• Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation

AI Engineer 4, AI Foundations

Data Scientist

• Partner with data scientists, software engineers, and product managers to deliver customer-focused products
• Leverage Python, Conda, AWS, H2O, Spark, and other technologies to analyze large volumes of numeric and textual data
• Build machine learning models through design, training, evaluation, validation, and implementation
• Translate complex data science work into tangible business goals
• Develop real-time customer behavioral prediction and valuation forecasting solutions
• Collaborate with business, engineering, analytics, product, and data science/quant teams

Manager, Data Science – US Card Customer Management

• Apply strategic and analytical skills to major company challenges
• Lead the Retail Bank’s Product Analytics team as a hands-on practitioner
• Shape product development, quantify new opportunities, and identify upcoming challenges
• Partner with teams to prioritize product development, set goals, and understand impact
• Drive product-led growth through rigorous analytical approaches, statistical methodologies, and opportunity frameworks
• Influence product direction, investment decisions, and the Retail Bank’s product vision
• Partner with Product Management and Engineering to identify high-leverage growth opportunities
• Translate behavioral data and user funnels into product recommendations and feature requirements
• Set methodological standards for experimentation
• Design, analyze, and interpret complex A/B/n tests using advanced statistical techniques
• Query, clean, and transform complex datasets using SQL, Python, or R
• Develop statistical models, user segmentation frameworks, and exploratory analyses
• Drive cross-functional alignment across Product, Technology, Research, and Finance
• Facilitate peer review gates and establish standardized KPI frameworks
• Synthesize data insights into strategic narratives and communicate customer value and long-term business impact

Business Manager, Product Analytics

• Partner cross-functionally with teams throughout Capital One to validate business-impacting analytical tools
• Refine an enterprise-wide analytical tools validation framework
• Plan and execute validation projects
• Assess the quality and risk of analytical tool methodologies and tool usage across Capital One
• Understand technical issues in analytical tools and assess tool risks and opportunities
• Research, identify, and prototype industry best practices for emerging analytical tools
• Communicate results through validation reports and presentations
• Leverage education, colleagues, and training opportunities to develop solutions to business problems
• Coach and mentor junior staff
• Handle multiple concurrent projects independently and in teams

Senior Manager – Data Analytics

• Design, build, and deliver machine learning models and components solving real-world business problems
• Collaborate with Product and Data Science teams
• Inform ML infrastructure decisions using knowledge of modeling techniques, data, feature selection, training, tuning, dimensionality, bias/variance, and validation
• Write and test application code
• Develop and validate ML models
• Automate tests and deployment
• Collaborate on cross-functional Agile teams to create and enhance big data and ML applications
• Retrain, maintain, and monitor production models
• Leverage or build cloud-based architectures, technologies, and platforms for optimized ML models at scale
• Construct optimized data pipelines feeding ML models
• Apply continuous integration and continuous deployment practices, including test automation and monitoring
• Ensure code is well-managed, models are risk-governed, and ML follows Responsible and Explainable AI best practices

Machine Learning Engineer 4, Manager

• Design, build, and deliver ML models and components solving real-world business problems with Product and Data Science teams
• Build and scale multi-tenant platforms for large-scale ML model training and serving
• Inform ML infrastructure decisions using knowledge of modeling techniques, data and feature selection, training, tuning, dimensionality, bias/variance, and validation
• Write and test application code, develop and validate ML models, and automate tests and deployment
• Collaborate on cross-functional Agile teams to create and enhance big-data and ML software
• Retrain, maintain, and monitor production models
• Leverage or build cloud-based architectures, technologies, and platforms for optimized ML models at scale
• Construct optimized data pipelines feeding ML models
• Apply CI/CD, test automation, and monitoring practices for successful deployment
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI best practices
• Use programming languages such as Python, Scala, or Java

Machine Learning Engineer, Level 5

Security Engineer

• Develop and communicate risk-based, data-driven strategies and outcome-driven roadmaps for enterprise security services, solutions, and capabilities
• Analyze and select alternatives based on threat, risk, desirability, feasibility, and viability
• Keep abreast of industry advances to address the threat landscape
• Advise executive decision-makers on business and technology risk grounded in threat
• Facilitate build-versus-buy decisions for business and technology risks
• Develop and lead RFIs/RFPs to assess the market and procure cyber solutions
• Lead product backlog grooming and translate cybersecurity and customer objectives into initiatives and epics with design details and acceptance criteria
• Maintain technical and risk credibility with the team and enterprise partners
• Collaborate with stakeholders, developers, and engineers to ensure services meet evolving product needs
• Iterate quickly and take products through production and scale
• Establish product value propositions, metrics, and key indicators
• Recruit, motivate, mentor, and lead team members

Manager, Cyber Security – Product Network Security

Principal

• Support the creation and execution of strategic and innovative solutions for internal customers, identifying value-generating opportunities such as savings, efficiencies, or revenue growth
• Negotiate basic contracts for a broad range of products and services
• Serve as a point of contact for third parties and manage effective governance between Capital One and assigned third parties
• Partner with business units and internal risk groups to ensure third-party relationships comply with relevant policies and procedures
• Monitor and ensure successful delivery against third-party contractual obligations
• Manage policy requirements for all managed third-party relationships
• Plan and lead periodic reviews and assessments of supplier relationships
• Maintain awareness of the external marketplace and perform basic sourcing activities where appropriate

Principal Associate, Supplier Management

Network Engineer

• Architect and design end-to-end connectivity systems with associated security and operational frameworks
• Use software development practices to design upstream-system interactions and build configuration frameworks for deployment, change, and maintenance
• Define implementation standards across vendor platforms, cloud environments, and in-house software
• Define protocol implementation patterns across campuses, WAN/SD-WAN, colocation facilities, data centers, cloud, and SaaS-fronted services
• Own the architecture and lifecycle of enterprise and cloud network ecosystems across connectivity, security, and automation layers
• Develop and enforce standards and best practices for routing, security segmentation, and inter-cloud traffic flow
• Lead critical incident response and ensure root-cause analysis produces durable reliability improvements
• Partner with Cloud, Security, and SRE teams on observability, telemetry, and self-healing automation
• Mentor and upskill engineers on advanced networking, reliability, and automation principles

Network Engineer 5

• Drive the strategy and implementation of AI-readiness initiatives for the Discover Personal Loans business
• Centralize, document, and optimize data for advanced analytical modeling
• Modernize monitoring and governance processes by automating manual controls and establishing scalable data abstraction layers
• Champion data quality and standardization efforts for core personal-loan data
• Leverage analytics to improve customer experience
• Lead and mentor a team of junior analysts
• Manage projects through their full life cycle and deliver end results
• Collaborate across business units
• Mine large datasets to identify crucial organizational information
• Implement process, data, and reporting improvements
• Work with databases containing millions to billions of records
• Improve operational efficiency and effectiveness
• Conduct process and test analyses
• Extract and analyze data to evaluate product offerings
• Consult on new production and data-storage systems
• Develop and implement strategic tests for products, initiatives, and marketing materials

Financial Planning and Analysis

• Develop repeatable processes, frameworks, and templates to improve team efficiency as Finance systems and processes integrate with Discover
• Establish reporting cadences with Tech and Finance partners
• Communicate key variances and drivers to help leaders remain fiscally well managed
• Analyze financial impacts of platform convergence in unstructured environments
• Deliver insights to senior leaders when information is evolving or imperfect
• Analyze and explain key cost drivers and monthly variances
• Recommend actions to the business and manage regular reporting processes
• Manage budgeting, forecasting, and Profit & Loss (P&L)
• Assist in developing presentations communicating analysis results to senior audiences
• Clearly articulate analysis outcomes to leadership teams
• Handle ad-hoc requests and special projects
• Independently manage and prioritize workload

Principal Associate, Financial Analysis

Full-stack Engineer

• Lead a portfolio of diverse technology projects and a team of developers with experience in distributed microservices and full-stack systems
• Create solutions that help meet the company’s regulatory needs
• Stay current with technology trends, experiment with and learn new technologies, and participate in internal and external technology communities
• Mentor members of the engineering community
• Collaborate with digital product managers to deliver robust cloud-based solutions serving millions of Americans
• Use programming languages, databases, container orchestration, and AWS tools and services to build solutions
• Provide technical leadership and direction across Agile teams
• Set standards for design, development, and operational excellence
• Partner with architects to define service boundaries, integration patterns, and reusable frameworks
• Identify improvement opportunities, implement targeted refactoring, reduce technical debt, and enhance system performance
• Influence long-term technology strategy through evaluation of emerging tools and practices in a “you build it, you own it” environment

Bilingual (Spanish) Senior Manager, Account Executive Operations

Machine Learning Engineer 4 – Intelligent Foundations and Experiences

Manager, Data Analysis – Personal Loans

AI Engineer 4, AI Foundations

Manager, Data Science – US Card Customer Management

Business Manager, Product Analytics

Senior Manager – Data Analytics

Machine Learning Engineer 4, Manager

Machine Learning Engineer, Level 5

Manager, Cyber Security – Product Network Security

Principal Associate, Supplier Management

Network Engineer 5

Manager, Data Analysis – Personal Loans

Principal Associate, Financial Analysis

Full-stack Engineer 4 – Manager, IC