Senior Data Scientist – AI & ML

Hybrid

Full-time

Role Summary

You’ll be one of the founding data scientists on our brand-new ML team, building Payoneer’s 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 the frameworks that evaluate our GenAI and LLM-based agents. You’ll set the modeling standards, technical practices and culture the team will scale on, and help hire the data scientists who come next. It’s a great fit if you’re excited to build something new and own it end to end.

What You’ll Do

• Build evaluation frameworks for GenAI and LLM agents – golden datasets, LLM-as-judge, and quality, safety and compliance monitoring
• Own the full ML model lifecycle, from research and experimentation through production deployment and monitoring
• Design and deploy ML models that solve complex business problems across payments, risk and customer analytics
• Work with the Tech Lead to define core modeling methodologies, analytical standards and research practices
• Set up the team’s approach to feature engineering, model validation, experimentation, A/B testing and performance monitoring
• Lead exploratory research to uncover insights, identify ML opportunities and evaluate new techniques
• Partner with business stakeholders to translate problems into data science solutions and communicate findings clearly
• Shape the team’s culture and documentation standards, and help interview, onboard and mentor new data scientists

Who You Are

• 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 – 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 – 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
• Sc. or M.Sc. in Computer Science, AI, Data Science, Mathematics or a related quantitative field
• Experience in fintech or financial services – an advantage