Machine Learning Engineering Manager - Fraud Detection

Klarna Bank AB

What you will do Perform independent end-to-end validation of fraud detection ML models, including conceptual soundness, data integrity, feature engineering, model development, deployment design, and monitoring frameworks. Develop challenger models. Review and challenge first-line fraud model methodologies, assumptions, and implementation choices (e.g., scikit-learn, LightGBM, graph models, anonaly detection techniques, GenAI components). Build and deploy agentic AI tools to support model validation workflows — automating review of model documentation and code, surfacing risks and inconsistencies. Assess model performance using appropriate fraud metrics (e.g., precision/recall, ROC-AUC, PR-AUC, cost-sensitive metrics, fraud rate capture, business impact trade-offs). Evaluate model stability, drift detection, retraining strategies, and production monitoring practices. Independently replicate model results where necessary and conduct challenger analyses to assess model robustness and limitations. Review large-scale transaction datasets and feature pipelines (e.g., >100M transactions, hundreds of features) to assess data representativeness, leakage risks, and bias. Evaluate model governance documentation, explainability approaches, and transparency — including regulatory compliance related to model risk, fairness, and data privacy. Validate new technologies applied in fraud detection, such as Graph Networks, Behavioral Biometrics, Anomaly Detection, and GenAI-based systems. Assess controls around CI/CD pipelines, deployment processes (e.g., Docker, Jenkins), and cloud environments (e.g., AWS SageMaker, S3, Athena, Lambda). Develop and maintain validation frameworks, testing standards, and model performance monitoring tools (e.g., SQL, PySpark, Python-based validation libraries). Collaborate closely with first-line fraud data scientists, ML engineers, product, and business stakeholders to ensure transparent communication of model risks and validation findings. Provide actionable recommendations and formally document validation outcomes in line with internal model governance standards and external regulatory expectations. Stay up to date with evolving fraud typologies, emerging ML/AI techniques, and regulatory developments in model risk management. Who you are Advanced degree (Master’s or PhD) in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Physics, or Engineering. 3+ years of hands-on experience in fraud-related modeling (e.g., transaction fraud, account takeover, identity fraud, payments fraud etc). Strong expertise in machine learning methods used in fraud detection, including tree-based models (e.g., LightGBM), anomaly detection, graph/network models, and advanced ML techniques. Deep understanding of the end-to-end ML lifecycle — from conceptual design and feature engineering to production deployment and monitoring — with the ability to critically challenge each stage. Strong programming skills in Python and SQL; experience with PySpark/Spark and large-scale data processing. Experience building agentic AI workflows. Familiarity with cloud-based ML platforms (e.g., AWS SageMaker, Lambda, S3, Athena) and production deployment workflows. Strong knowledge of model validation principles, model risk governance frameworks, and regulatory expectations. Experience assessing model bias, fairness, explainability, and privacy risks. Excellent analytical thinking and structured problem-solving skills, with the ability to assess complex models and clearly articulate risks and limitations. Strong communication skills, capable of translating technical findings into clear, actionable insights for senior stakeholders and non-technical audiences. Ability to work independently while constructively challenging first-line teams in a collaborative manner. Awesome to have Experience in BNPL, credit cards, payments, or other transaction-heavy financial products. Experience validating models in highly regulated environments. Experience mentoring junior validators or leading validation reviews. Exposure to inference of rejected transactions and understanding of fraud/credit overlap. Familiarity with AI governance frameworks and emerging AI regulatory requirements.

Adyen

This is Adyen Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition.  For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. HEAD OF ENGINEERING, ADYEN PROTECT We are seeking a visionary and highly technical Head of Engineering to lead the development and scaling of Adyen Protect. Adyen Protect is our ML-first product suite designed to detect and mitigate payment fraud in real-time across billions of transactions. In this role, you will lead a specialized domain of four high-impact engineering teams dedicated to staying ahead of increasingly sophisticated global fraud networks. You will be the strategic driver behind our risk engine, ensuring that our ML models are not only accurate and performant but also transparent and actionable for the world's largest merchants. This is a role for a leader who thrives at the intersection of big data, predictive modeling, and high-availability systems. You will be responsible for ensuring that as Adyen grows, our ability to protect our merchants and their consumers remains world-class. WHAT YOU’LL DO: * Vision & Strategy: Define and execute the technical roadmap for Adyen Protect, aligning Machine Learning capabilities with the evolving needs of global e-commerce and regulatory landscapes. * Scale ML Operations: Lead four multidisciplinary teams in building and maintaining scalable infrastructure for model training, real-time scoring, and automated experimentation (A/B testing) at the millisecond level. * Engineering Excellence: Establish and maintain high standards for engineering quality and reliability. Ensure our fraud detection systems are resilient, low-latency, and capable of processing massive data volumes without downtime. * Cross-Functional Leadership: Partner closely with Product Leaders, Risk officers, and other parts of the business to translate complex fraud patterns into robust technical features and automated mitigation strategies. * Team Development: Mentor and grow a high-performing organization of engineers and engineering managers. Foster an inclusive environment that encourages rapid prototyping, ethical AI practices, and continuous learning. * Merchant Advocacy: Interface with our largest global merchants to understand their unique risk profiles and ensure Adyen Protect provides the granular control and insights they need to grow their business safely. WHO YOU ARE: * ML & Data Enthusiast: You have a deep understanding of Machine Learning lifecycles, from data engineering and feature extraction to model deployment and monitoring. * Proven Leader: You have a track record of leading complex engineering organizations and managing other managers, specifically within high-growth fintech or SaaS environments. * Strategic Problem Solver: You can balance the immediate need for fraud mitigation with the long-term goal of reducing "false positives" to maximize merchant revenue. * Tech-Fluent: You remain technically literate and are comfortable diving into architecture discussions regarding distributed systems, data pipelines, and real-time processing. * Collaborative: You excel at building bridges between technical and non-technical stakeholders, explaining the "why" behind complex (ML) decisions. MINIMUM QUALIFICATIONS: * 7+ years of technical experience, including significant tenure as a hands-on software or machine learning engineer. * 5+ years of experience in manager-of-managers roles, overseeing multiple engineering teams. * Experience with High-Scale Systems: Proven track record of managing products that handle high-throughput, low-latency workloads (e.g., payments, cybersecurity, or real-time bidding). * Domain Expertise: Familiarity with fraud detection, risk management, or identity verification systems is highly preferred. Our Diversity, Equity and Inclusion commitments  Our unique approach is a product of our diverse perspectives. This diversity of backgrounds and cultures is essential in helping us maintain our momentum. Our business and technical challenges are unique, and we need as many different voices as possible to join us in solving them - voices like yours. No matter who you are or where you’re from, we welcome you to be your true self at Adyen.  Studies show that women and members of underrepresented communities apply for jobs only if they meet 100% of the qualifications. Does this sound like you? If so, Adyen encourages you to reconsider and apply. We look forward to your application! What’s next? Ensuring a smooth and enjoyable candidate experience is critical for us. We aim to get back to you regarding your application within 5 business days. Our interview process tends to take about 4 weeks to complete, but may fluctuate depending on the role. Learn more about our hiring process here. Don’t be afraid to let us know if you need more flexibility.   This role is based out of our Amsterdam office. We are an office-first company and value in-person collaboration; we do not offer remote-only roles.

Stripe

WHO WE ARE ABOUT STRIPE Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.   ABOUT THE TEAM The Radar ML team builds the fraud detection models that protect Stripe's $1.9 trillion payment network from fraud. The team owns 10+ real-time deep learning models that must constantly evolve to stay ahead of fraudsters. Each ML improvement translates directly into dollar impact for Stripe and its users.  The team's models also power the Radar product suite that tens of thousands of businesses use to screen payments and manage fraud. Radar is growing fast, and the team is actively building new products like defenses against AI token theft, free trial abuse, and programmatic attacks. WHAT YOU’LL DO In this role, you will own ML work across the full lifecycle: researching new fraud patterns, building and deploying models, and sharing results directly with top Stripe customers. You will have opportunities to optimize Stripe’s most intensive ML models, and opportunities to ship 0-to-1 products from scratch.  RESPONSIBILITIES * Build, train, evaluate, and deploy ML models that detect fraud across Stripe’s global payments network * Research emerging fraud patterns like token theft and develop ML solutions to address them * Apply advances in deep learning to improve model quality and detection rates at scale * Co-build new fraud and abuse products directly with top users WHO YOU ARE We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. MINIMUM REQUIREMENTS * 6+ years of industry experience training, evaluating, and deploying ML models in a production environment * Proficiency in Python and common data and ML frameworks like SQL, Spark, and PyTorch * Strong knowledge of production ML systems; and data analysis, statistics, and experiment design fundamentals * Active interest in the latest ML developments, and how they can be leveraged to solve business problems PREFERRED QUALIFICATIONS * Experience building and optimizing real-time, low-latency ML infrastructure at scale * Strong software engineering skills and ability to design ML solutions through entire product stack * Experience applying ML to fraud detection, risk modeling, or a closely related domain * Experience designing ML products used by millions of users