We are looking for a Data Scientist to drive performance optimization for our clients AI Engine. This high-impact role will tackle complex computational bottlenecks and contribute to the re-engineering of simulation, training, and post-processing pipelines for large-scale industrial AI models. The candidate will collaborate with a multidisciplinary team to deliver quantifiable improvements in speed, scalability, and memory efficiency. Responsibilities: Analyze and optimize AI engine code, focusing on removing performance bottlenecks in simulation, training, and post-processing workflows. Refactor sequential code and nested loops into efficient, vectorized operations, leveraging advanced knowledge in linear algebra and matrix decomposition. Diagnose and resolve computational inefficiencies related to GPU/TPU, non-vectorized data handling, and mixed framework operations (JAX, NumPy, Pandas). Develop and implement solutions for ODE (ordinary differential equation) solvers, optimization algorithms, and batch processing strategies. Lead root cause analysis for performance limitations and propose alternative algorithmic strategies (including MILP/LP, decomposition techniques, and alternative ODE solvers). Guide remediation and refactoring efforts, including memory optimization, JIT compilation, and data type standardization. Document improvements, monitor ongoing performance, and contribute to a roadmap for further scalability enhancements.