Principal Machine Learning Engineer at Grab
Previously research engineer in computational fluid dynamics and renewable energy forecasting
Doctorate in scientific computing from TU Kaiserslautern, with research at von Karman Institute
Areas of expertise - across roles in tech, energy, aerospace, and academia
Time-Series: Production forecasting, anomaly detection and RCA across ride-hailing, renewable energy, weather, and business metrics.
MLOps: Model lifecycle, batch pipelines, online inference, and real-time streaming for high-throughput workloads.
Scientific Computing: Optimization, adjoint methods, automatic differentiation, and numerical solvers (fluid dynamics and structural mechanics).