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Payoneer is a global financial platform founded in 2005 that simplifies cross-border business operations, serving customers and partners in over 190 countries. The company removes friction from financial workflows including global payments, compliance, multi-currency management, workforce management, working capital, and business intelligence.
You will be one of the founding data scientists on Payoneer's brand-new ML team, building AI and data science capabilities from the ground up. Working closely with the Tech Lead, you'll develop and deploy models across payments, risk, and customer analytics, and build evaluation frameworks for GenAI and LLM-based agents. You'll set the modeling standards, technical practices, and culture the team will scale on, and help hire subsequent data scientists.
Key responsibilities include:
- Building evaluation frameworks for GenAI and LLM agents, including golden datasets, LLM-as-judge approaches, and quality, safety, and compliance monitoring
- Owning the full ML model lifecycle from research and experimentation through production deployment and monitoring
- Designing and deploying ML models that solve complex business problems across payments, risk, and customer analytics
- Working with the Tech Lead to define core modeling methodologies, analytical standards, and research practices
- Setting up the team's approach to feature engineering, model validation, experimentation, A/B testing, and performance monitoring
- Leading exploratory research to uncover insights, identify ML opportunities, and evaluate new techniques
- Partnering with business stakeholders to translate problems into data science solutions and communicate findings clearly
- Shaping team culture and documentation standards, and helping interview, onboard, and mentor new data scientists
This is an opportunity to build something new end-to-end with significant ownership and influence over technical direction.
REQUIREMENTS:
- 5+ years of experience in Machine Learning, Data Science, or a related technical role
- Proven track record of building and deploying ML models in production environments
- GenAI/LLM evaluation experience, including benchmark datasets, LLM-as-judge or human-in-the-loop evaluation, and monitoring of RAG and agent systems
- Strong proficiency in Python and SQL, and in ML frameworks such as TensorFlow, PyTorch, and Scikit-learn
- Experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools and practices
- Technology leadership experience—a history of making tech stack decisions and establishing development practices that scale
- Excellent communication skills with the ability to explain technical concepts and influence technical decisions
- Entrepreneurial, self-starter mindset; excited to build from scratch and comfortable with ambiguity and ownership
- B.Sc. or M.Sc. in Computer Science, AI, Data Science, Mathematics, or a related quantitative field
- Experience in fintech or financial services is an advantage
