Senior ML Data Scientist (Credit Risk)

Zed; San Francisco (CA); On-site; USD 130,000 - 160,000; Full time

Benefits offered by this job

Equity options

Comprehensive benefits package

Flexible working hours

Job summary

Zed, based in San Francisco, is seeking a Senior ML Data Scientist to join their innovative team. In this role, you will work on advanced credit models, leveraging AI to redefine financial services. Ideal candidates will have over 7 years in ML with hands-on experience in deploying state-of-the-art models.

You'll contribute to building robust fraud detection mechanisms and work closely with engineering teams. This is a senior position with significant influence on technical decisions, ideal for those passionate about transforming the banking landscape.

Qualifications

• 7+ years of experience in applied ML or data science focusing on credit risk and fraud.
• Hands-on experience deploying LLMs and transformer architectures in production.
• Proficiency in Python and SQL; knowledge of ML frameworks.

Responsibilities

• Collaborate on credit decisioning and fraud detection models.
• Own data preparation pipelines for modeling inputs.
• Design and deploy fraud detection models in real time.

Skills

Applied ML or data science experience

Hands-on LLM and transformer-based experience

Proficiency in Python and SQL

Strong feature engineering skills

Statistical foundation

Experience building production models

Model-driven vs rule-based approaches

Leadership and communication skills

Tools

XGBoost

LightGBM

PyTorch

Job description

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 detection

• Own data preparation pipelines for model inputs — including using NNs and LLMs to represent transaction data as vector embeddings for quantitative analysis

• Experiment with and deploy neural network and transformer-based architectures in the underwriting process

• Build agent scaffolding and harnesses within underwriting workflows — this is active, in-production experimentation, not research

• Design and deploy fraud detection models combining rule-based systems and ML to identify suspicious activity in real time

• Engineer features from structured and unstructured data sources

• Develop monitoring systems to keep models accurate and reliable in production

• Partner with engineering and risk operations to integrate model outputs into decisioning systems

• Influence 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 services

• Hands-on experience with LLMs, embeddings models, or transformer-based architectures — not just familiarity, but production or near-production deployment

• Proficiency in Python and SQL; experience with frameworks such as XGBoost, LightGBM, PyTorch, or similar

• Strong feature engineering skills — you know how to extract signal from messy, sparse, or heterogeneous financial data

• Solid statistical foundation: anomaly detection, supervised classification, model calibration, experimentation design

• Experience building and monitoring production models including alerting on performance degradation and concept drift

• Familiarity with both rule-based and model-driven approaches — and when to use each

• A point of view on how ML systems should be built — you can articulate tradeoffs, push back on bad decisions, and bring junior team members along

• Comfort operating as a senior ML voice at an early-stage company — you own problems end-to-end and set the standard for others

• Experience 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.