Data Scientist
Zed · Manila
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
As a Data Scientist at Zed, you will play a critical role in shaping how data and AI power our core financial products. In your first six months, you will own the development of data science and machine learning models that directly impact credit risk, decisioning, and product performance. You will work closely with Product, Risk, Engineering, and Finance partners to translate complex business problems into robust, production-ready data solutions in a fast-moving, resource-constrained startup environment.
What You’ll Do
Build, validate, and iterate on machine learning models that support core financial use cases such as credit risk assessment, underwriting, and portfolio performance.
Partner closely with cross-functional stakeholders—including Product, Risk, Engineering, and Finance—to understand business objectives, define success metrics, and deliver data-driven insights.
Take end-to-end ownership of data science projects, from problem formulation, requirement setting, and data exploration to model development, evaluation, and handoff for production.
Leverage modern AI techniques, including LLMs and generative AI workflows, to develop innovative solutions where appropriate (e.g., fine-tuning, RAG, or agentic systems).
Continuously improve Zed’s data science practices by staying current with industry advancements and helping mature internal tools, processes, and standards.
What You Bring
Experience
3+ years of professional experience in data science, machine learning, or applied AI, or equivalent graduate-level research with significant hands-on model development.
Demonstrated experience building ML models from the ground up .
Technical Depth
Strong proficiency in Python and common data science and ML libraries; experience with SQL and data analysis workflows.
Hands-on experience with machine learning model development and evaluation
Exposure to or experience with LLMs and generative AI, including fine-tuning, prompt engineering, RAG, or agentic systems.
Problem-Solving & Communication
Excellent analytical and problem-solving skills, with the ability to translate ambiguous business problems into structured data science solutions.
Strong communication skills and comfort explaining technical concepts and results to non-technical stakeholders.
Education
Bachelor’s degree in a quantitative field such as data science, statistics, computer science, engineering, or equivalent practical experience. Advanced degrees are a plus.
Bonus (Nice-to-Haves)
Experience in financial services, particularly credit risk modeling, scorecard development, or lending-related analytics.
Familiarity with advanced AI techniques such as causal inference, reinforcement learning, or explainable AI.
Experience working in startups or constrained environments (e.g., small datasets, cold-start problems, limited resources).
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.