Fraud Operations Associate Sdc in United States

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Director, Customer Performance Who is Mastercard? Mastercard is a global technology company in the payments industry, operating as the world’s fastest payment processing network. Our mission is to connect and power an inclusive, digital economy that benefits consumers, financial institutions, merchants, governments and businesses in more than 210 countries and territories. Safety and security are top priorities and we continually find opportunities to make transactions safe, simple, smart, and accessible. Overview: Franchise is core to all Mastercard business. Franchise enables business growth and value, fostering interactions with customers which provides ongoing trust and confidence in the evolving payments and data driven ecosystem. This role enables high-risk acquirers to participate in the Mastercard global ecosystem by engaging directly with customers to improve ecosystem performance and strengthen participants’ understanding of business requirements. About the Role: With the growth of merchant fraud, acquirers must strengthen their screening of new merchants, as well as closely monitor new risk signals in transaction data, while ensuring their merchants remain compliant with local laws and Mastercard standards. To help acquirers achieve this, the Franchise team is looking for a fraud

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role We’re looking for a Senior Applied Scientist to help drive the future of credit applied science at Ramp. In this role, you will design, build, and optimize the models that power our credit risk systems, helping us make faster, smarter, and more scalable risk decisions for our customers. You’ll work at the intersection of machine learning, statistics, economics, and product strategy. This role requires strong technical depth as well as close collaboration with business, product, data, and engineering partners. You will help identify high-impact opportunities, translate ambiguous business problems into rigorous modeling work, and ship models that operate reliably in production. Applied scientists at Ramp focus on solving quantitative problems across credit, fraud, growth, and our core product by applying the right mix of machine learning, causal inference, structural modeling, and optimization. What You'll Do Design, build, and optimize machine learning models that support credit risk decisioning and portfolio management at Ramp Own the

About the Team The Applied Foundations team at OpenAI is dedicated to ensuring that our cutting-edge technology is not only revolutionary but also secure from a myriad of adversarial threats. We strive to maintain the integrity of our platforms as they scale. The Applied Foundations team is at the front lines of defending against financial abuse, scaled attacks, and other forms of misuse that could undermine the user experience or harm our operational stability. Integrity Foundations provides the core building blocks and infrastructure for this work. About the Role At OpenAI, our mission is to advance AI in a way that is safe, reliable, and aligned with broad societal values. The applied foundations role is crucial for maintaining the trustworthiness of our platforms. You will be pivotal in developing robust defenses against a spectrum of adversarial behaviors that threaten our ecosystem. In this role, you'll work with our entire engineering team to design and implement systems that detect and prevent abuse, promote user safety, and reduce risk across our platform. You'll be at the forefront of our efforts to ensure that the immense potential of AI is harnessed in a responsible and sustainable manner. In this role, you will: Develop and enhance systems to detect and prevent various forms of abuse including financial fraud, botting, and scripting. Collaborate with cross-functional teams to design solutions that protect against and mitigate adversarial attacks without compromising user experience. Assist with response to active incidents on the platform and build new tooling and infrastructure that address the fundamental problems. You might thrive in this role if you: Have at least 3 years of professional software engineering experience. Have experience setting up and maintaining production backend services and data pipelines. Have a humble attitude, an eagerness to help your colleagues, and a desire to do whatever it takes to make the team succeed. Are self-directed

CLEAR is building THE secure identity company of the future. Our mission is to make experiences safer and easier—physically and digitally. With more than 43 million Members and a growing network of partners across the world, CLEAR's secure identity platform is transforming the way people live, work, and travel. Whether it’s at the airport, stadium, or throughout your everyday life, CLEAR unlocks the magic of frictionless experiences. We’re looking for an experienced Machine Learning Engineer to help us build the next generation of products which will go beyond just ID and enable our members to leverage the power of a networked digital identity. As a Machine Learning Engineer at CLEAR, you will participate in the design, implementation, testing, and deployment of applications to build and enhance our platform - one that interconnects dozens of attributes and qualifications while keeping member privacy and security at the core. A brief highlight of our tech stack: Python / Postgres / Snowflake / dbt AWS SageMaker and MLflow What you'll do: Own and drive the foundational work of a ML system at CLEAR Design, build and deploy ML models for various applications, such as document and image processing, fraud detection. Develop and implement robust data pipelines at a variety of scales, including collection, pre-processing, transformation, and feature engineering Partner with product and other stakeholders to uncover requirements, to innovate, and to solve complex problems Have a strong sense of ownership, responsible for architectural decision-making and striving for continuous improvement in technology and processes at CLEAR What you're great at: 3+ years of experience building, operating and scaling ML models for consumer applications, particularly those with experience building end-to-end systems Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance Expertise in best practices for feature enginee

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Manager, Product Management Overview: The Security Solutions Organization (SSO) encompasses Mastercard's (NYSE: MA) security offerings beyond the transaction. We provide customers across industries and geographies with a tailored portfolio of solutions to address their business pain points. SSO provides cutting edge services in the areas Cyber Security, Identity & Fraud, and Disputes. Anchored on thinking big and scaling fast, our team is responsible for ensuring our Global products and services fit the needs specific to the North America region and are tailored to the needs across our customer base including retailers, issuers, acquirers, processors, gateways, and other ecosystem partners. We are looking for a seasoned product manager within the Security Solutions space to play a key role in the commercialization/go-to-market execution for its products. This role is based in and supports the North American Market (NAM), and is designed to support a remote employee operating within the NAM business environment. The team’s goal is to ensure the long-term co

About the role We’re looking for an engineering manager to lead a team building software systems that detect and prevent harmful misuse of frontier AI models—before incidents occur. This is a builder’s role: you’ll lead engineers shipping production services, detection pipelines, and mitigation mechanisms that protect frontier model integrity and reduce high-severity misuse risk. While this work intersects with frontier model development, security and risk, we’re explicitly seeking someone with a software engineering foundation who is comfortable building reliable systems that can operate at billions of users scale. In this role you will: Lead a team of software engineers building detection + mitigation systems for frontier model misuse, with an emphasis on model IP protection / distillation detection and emerging risk surfaces from autonomous agents. Set the technical roadmap and execution strategy: prioritize, design, ship, iterate, measure impact. Build production systems: services, pipelines, tooling, instrumentation, and automation that scale with frontier model usage. Partner deeply with Research and Product to translate evolving model capabilities into concrete tests, signals, and mitigations that can be deployed at scale. Drive strong engineering fundamentals: architecture, reliability, monitoring, performance, and operational excellence. Hire and grow an exceptional team across backend, data systems, and applied ML engineering domains as needed. Anticipate what breaks at scale as agentic workflows become more capable. You might thrive in this role if you: Experience building systems in adversarial, fast-evolving environments Are comfortable with ambiguity and novelty Have experience adjacent to security (e.g., abuse prevention, fraud, integrity, platform defense, auth/identity, malware/spam, adversarial environments) Communicate clearly and build trust quickly with senior stakeholders—pragmatic, collaborative, and calm under scrutiny. Significant experience

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. About the Team The Fraud Partnerships Pod is responsible for overseeing all traditional business development and partnerships for the Anti-Fraud Product Area (PA) at Plaid, focusing primarily on our product partnerships and data acquisition (supply side) efforts. The team supports all products under the Fraud Product Area's purview: IDV, Monitor, Layer, and Protect. About the Role You will have an opportunity to lead the strategy and execution of our supply side and "data in" efforts for our Fraud product area across four products (Plaid IDV, Monitor, Layer, and Protect). You will own all critical data partner relationship management with key stakeholders and be expected to grow them over time. You will be the internal quarterback driving alignment with key major internal stakeholders at Plaid, including Finance, BizOps, Commercial, Legal, and Risk. What You'll Do Spend the majority of your time working hand in hand with product leadership as well as other cross-functional leaders to operationalize our data partners for all fraud products at Plaid. Help our data science and research teams define the source data ecosystem for IDV and Protect according to our 3-year strategy. Help e

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Our Fraud team's mission is to help companies detect and prevent fraud using financial network data. We believe that transaction patterns, device signals, and behavioral data are an underleveraged tool in fraud prevention. The Fraud Consulting Lead is responsible for building and maturing our retro-as-a-service and POC program and driving adoption of our fraud products. The role sits between our data team and the customer (i.e., the role will not build the models, but will need to understand the output well enough to clearly and compellingly present the business case and work with technical stakeholders and customers.) Responsibilities: Own the retro and POC process end-to-end and collaborate with customers and internal stakeholders at Plaid Partner closely with customers to help them understand the ROI of Protect and make recommendations for implementing our fraud products Drive post-retro follow-through to convert retro results into production usage Serve as a feedback loop between customers and product to drive our Protect roadmap Qualifications: 5-10 years of experience in a customer-facing analytical role in fintech, financial services, or a related domain (e.g., software/tech) Experience worki

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. The Fraud Data team at Plaid builds the machine learning systems that power Plaid’s fraud detection products, leveraging insights from across Plaid’s network to help identify and stop fraud before it happens. Our team works across the full data science and machine learning lifecycle—from discovering new signals and experimenting with models to deploying and optimizing them in production. We continuously learn from real-world model performance and customer feedback to improve our systems and develop new ways to protect customers and consumers from evolving fraud threats. As a Senior Machine Learning Engineer on Plaid's Fraud Data team, you will develop models that improve fraud detection for our customers. You will identify predictive patterns in Plaid's network data and lead projects from initial experiments through model deployment and ongoing improvement. Investigate fraud patterns and model errors to identify new signals, improve detection, and expand coverage across customers and use cases. Develop training datasets and predictive features, addressing challenges such as incomplete labels, class imbalance, data leakage, and changing fraud behavior. Design, train, and tune model

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Fraud Data is the data science and machine learning team within Plaid’s Fraud organization, responsible for using data and ML to improve and scale Plaid’s fraud products. Within Fraud Data, the Customer & Product Intelligence team focuses on understanding product performance, uncovering customer insights, and enabling go-to-market teams with data-driven solutions. The team partners closely with customers and GTM teams on fraud analyses and proofs of concept, turning customer learnings into scalable, reusable product capabilities. We also build the metrics, analytics, and data foundations that measure product health, identify opportunities for improvement, and guide product decisions across Plaid’s Fraud portfolio. As a Data Science Manager, you will lead a team responsible for customer-facing data science and Fraud product analytics. You will set the team's roadmap, develop its data scientists, and remain involved in analytical methods, technical reviews, and customer investigations. You will: Set a 6–12-month roadmap with Product, Engineering, and GTM, and assign priorities and responsibilities across the team. Define product metrics, their underlying data, and reporting and

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. We are the Data team within Plaid’s Fraud organization. We build the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s network data to help identify and prevent fraud before it happens. Our team owns the end-to-end ML lifecycle, from feature pipelines and model training to production serving and monitoring, ensuring our systems are reliable, scalable, and built to support hundreds of customers and data partners. As a Data Scientist on the Fraud Data team, you will analyze customer and network traffic to understand how Plaid Protect performs across a range of use cases and customer segments. You’ll build dashboards and metrics that provide a clear, shared view of product performance, run backtests to evaluate performance and identify high-impact rules and model strategies, and generate insights that support customer growth and expansion. You’ll also design scalable data models and schemas to enable reliable analysis and reporting, while partnering closely with Product and Engineering to design and analyze experiments for new customer-facing features. Responsibilities: Work at the intersection of product analytics, machine learning, and fraud a

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. About the team The Product Sales team at Plaid is a specialist, solutions-focused sales organization responsible for driving adoption and growth of Plaid’s emerging products across both new and existing customers. This team plays a critical role in scaling products, translating customer needs into commercial opportunities, and accelerating Plaid’s next phase of growth through consultative, value-driven selling. About the role In this role, you will own the full sales cycle for Plaid’s Fraud solutions, driving new business and expansion within existing customers. You will act as a subject matter expert across fraud, risk, and identity verification, partnering closely with Account Management, Product, and Go-To-Market teams to identify high-impact use cases and deliver measurable customer value.This role is ideal for a self-starting seller who operates with ownership, thinks ahead, anticipates risk, and takes proactive action, while embracing a collaborative, win-as-a-team approach with fellow sellers and cross-functional partners. Responsibilities Own new business and expansion revenue for Plaid’s Fraud and Identity solutions across a defined territory. Build deep customer relationships and clearly a

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. As an Economy Fraud engineer, you defend Roblox from all types of fraud, including theft, scams, money laundering, and payment fraud. Roblox is a high-growth, unique product environment. You will be developing anti-fraud and abuse solutions for web, mobile, and 3D environments. This high impact work and your innovation is critical for the well-being of our community and to the future of our company. We aim for our users to have peace of mind that their communities and transactions are protected. Our defenses also protect our company’s rapid expansion and safeguard billions in revenue. Roblox’s virtual marketplace handles over 4 million transactions a day, and enables our top developers to make millions of dollars a year. Our team’s challenges are not just regular day-to-day technical challenges. Fraud and abuse approaches need to shift over time, depending on the current behaviors of fraudsters. As an Economy Fraud engineer, you will be in a data-driven environment developing both classical and novel approaches to detect and prevent this bad behavior. You Have: 4+ years of professional experience working with scalable, distributed systems Strong experience in large-scale, data-driven

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. We are the Fraud Data team within Plaid's Fraud organization. We leverage relationships and activity across Plaid’s network to develop machine learning models that help customers detect and prevent fraud while minimizing friction for legitimate users. Our team owns the full model lifecycle, from data processing and experimentation to feature pipelines, model serving, and monitoring. In this research role, you’ll partner closely with Machine Learning Engineers and Data Scientists to uncover new signals, explore and evaluate innovative modeling approaches, and translate promising research into production-ready solutions that strengthen fraud detection across Plaid’s network. As a Senior Research Scientist, you will lead applied research to develop next-generation fraud detection models across relational graphs, sequential events, images, and video data. You’ll design rigorous experiments and evaluation methodologies that reflect real-world fraud dynamics, while exploring state-of-the-art approaches such as Graph Neural Networks and Transformer-based foundation models. In close partnership with Machine Learning Engineers, you’ll translate promising research into production-ready solu

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. The team owns the full ML lifecycle—from feature pipelines and model training to production serving and monitoring—building reliable, scalable systems that deliver high-quality fraud detection as we grow to support hundreds of customers. As a Senior Machine Learning Engineer, you will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale. You’ll build robust observability, monitoring, and automated debugging capabilities, while leveraging AI-assisted tools to investigate complex system behavior and maintain high reliability. You’ll partner closely with ML Infrastructure, Data Science, and Product teams to execute critical technical initiatives and deliver scalable, high-impact ML solutions. Responsibilities: Build and scale machine learning systems that power a rapidly growing fraud detection product in a fast-paced environment. Solve complex technical challenges at the intersect