Full-Time Data Scientist - AI and Optimization

• 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.

• Masters in Mathematics, Operations Research, Computer Science, or related quantitative field.
• Deep expertise in matrix decomposition, numerical optimization, and ODEs, with a demonstrated ability to apply these in real-world computation.
• Strong proficiency in Python, with hands-on experience in JAX, and experience with GPU/TPU acceleration.
• Prior exposure to optimization in scientific computing, especially in re-engineering or scaling data pipelines.
• Familiarity with ensemble methods, batch and vectorized computation, and memory management in large datasets.
• Ability to communicate technical findings and lead cross-functional discussions for codebase improvement.

• PhD preferred
• Background in large-scale industrial AI or simulation platforms.
• Knowledge of MILP, LP decomposition, and alternative ODE solvers (e.g., diffrax, odeint).
• Experience transitioning AI workloads from CPU to GPU/TPU environments.
• Experience with JAX, PyTorch or TensorFlow as alternative computation frameworks.
• Experience with CI/CD practices for ML/AI pipelines, profiling tools (e.g., cProfile, memory_profiler, jax.profiler), and performance benchmarking.

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