Applied Scientist / Applied ML Engineer

Description: Design, build, and deploy machine learning models for pricing, bidding, and decisioning on a cross-border payments platform. Own problems end to end, collaborating closely with Product and Backend Engineering. Requirements: 3-7 years of experience as an ML Engineer, Applied Scientist, or Data Scientist. Bachelor's or Master's in Computer Science, Machine Learning, Mathematics, Statistics, or equivalent. Strong Python skills (pandas, NumPy, scikit-learn) and experience with PyTorch or TensorFlow. Strong ML fundamentals and experience in model design and production. Hands-on experience with data pipelines (Airflow, Spark) and strong SQL skills. Experience integrating ML into REST or gRPC APIs and microservices. Ability to design and interpret experiments with statistical rigor. Strong problem-solving and communication skills. Benefits: Real ML in production with direct impact on pricing and risk decisions. Ownership of core models with influence on architecture and roadmap. Work with strong engineering peers on complex optimization problems in a high-growth fintech.