— Design and build core ML models and pipelines as part of a true greenfield direction, not a maintenance handover
— A real-time ML decisioning system with direct revenue impact
— A recommendation engine and other ML-driven features that lift merchant conversion and revenue
— Hands-on experimentation — from idea, through A/B testing, to production
— Integrations of cutting-edge AI technologies to drive innovation
— Close partnership with Product, Engineering, and the Data Platform team (who handle ML Ops) to solve hard modelling problems and build orchestrated training and analytics pipelines
— Clear communication of technical results to both engineers and cross-functional stakeholders