Senior ML Data Scientist (Credit Risk)

About Zed Zed is building the first AI-native, licensed neobank in the Philippines designed to democratize access to premium financial services for young professionals in global markets. The current banking system is broken, often shutting out the world’s youngest and fastest-growing consumer classes—we’re here to fix it. Our team is uniquely positioned to solve this. We are Stanford engineers and former YC founders who have spent our careers at the intersection of banking and hyper-growth startups like Square, Facebook, and Box. We’ve been here before, having previously built and exited Symple (YC W'17), a fast-growing B2B payments company. We are backed by world-class investors, including Accel, Valar, Immad Akhund (Mercury), Dalton Caldwell (Y Combinator), and Kunal Shah (Cred). The Role Zed underwrites credit using foundation models that profile risk from transaction data, financial documents, and other structured and unstructured sources — not credit scores. That means our ML stack looks less like a traditional bank's and more like a modern AI system: embedding models, transformer architectures, and LLM-assisted data pipelines sitting alongside classical credit and fraud models. We're hiring a Senior ML Data Scientist to work directly with our data lead across all of it — core credit models, account management, and fraud detection — with a particular focus on pushing the frontier of how we represent and reason about financial data. This is a senior role, which means we expect you to have opinions about the stack, shape how we build, and set the technical bar for ML at Zed as the team grows. If you're the kind of person who's deploying neural networks and transformer-based models in production rather than just reading about them, this role was written for you. What You’ll Do Work closely with our data lead on the full risk model suite: core credit decisioning, account management, and fraud detectionOwn data preparation pipelines for model inputs — including using NNs and LLMs to represent transaction data as vector embeddings for quantitative analysisExperiment with and deploy neural network and transformer-based architectures in the underwriting processBuild agent scaffolding and harnesses within underwriting workflows — this is active, in-production experimentation, not researchDesign and deploy fraud detection models combining rule-based systems and ML to identify suspicious activity in real timeEngineer features from structured and unstructured data sourcesDevelop monitoring systems to keep models accurate and reliable in productionPartner with engineering and risk operations to integrate model outputs into decisioning systemsInfluence technical direction — you'll have a seat at the table when we make decisions about how ML is built and deployed at Zed What You Bring 7+ years of experience in applied ML or data science with a focus on credit risk, fraud, or financial servicesHands-on experience with LLMs, embeddings models, or transformer-based architectures — not just familiarity, but production or near-production deploymentProficiency in Python and SQL; experience with frameworks such as XGBoost, LightGBM, PyTorch, or similarStrong feature engineering skills — you know how to extract signal from messy, sparse, or heterogeneous financial dataSolid statistical foundation: anomaly detection, supervised classification, model calibration, experimentation designExperience building and monitoring production models including alerting on performance degradation and concept driftFamiliarity with both rule-based and model-driven approaches — and when to use eachA point of view on how ML systems should be built — you can articulate tradeoffs, push back on bad decisions, and bring junior team members alongComfort operating as a senior ML voice at an early-stage company — you own problems end-to-end and set the standard for othersExperience with emerging markets or data-sparse environments is a plus We hire exceptional people from diverse backgrounds because different perspectives build better products. If you’re excited about this role but don’t check every box, apply anyway. We value potential, ownership, and alignment with our values more than perfect résumés. We are an equal opportunity employer and do not discriminate based on legally protected characteristics. We provide reasonable accommodations throughout the hiring process. Compensation includes salary, equity, and benefits. Final offers are based on role scope, location, and experience.