Summary
Key Responsibilities
- Formulate complex optimization problems (nonlinear, nonconvex, stochastic, constrained, multi-objective).
- Build advanced optimization pipelines using :
o SciPy Optimize, PySwarms, mystic, pymoo, Bayesian Optimization.
Develop custom, complex solvers and hybrid algorithms leveraging open-source optimization frameworks.Implement objective functions, constraint models, surrogate models, and penalty formulations.Integrate optimization techniques into ML workflows (hyperparameter tuning, black-box optimization, surrogate modeling).Conduct convergence, sensitivity, robustness, and stability analysis of optimization methods.Scale optimization systems using Python, distributed computing, and numerical acceleration.Communicate complex mathematical concepts to cross-functional audiences.Required Qualifications
Masters / PhD in Operations Research, Applied Mathematics, Computer Science, Engineering, or related quantitative field.Expertise in nonlinear, global, evolutionary, and multi-objective optimization (e.g., NSGA-II / III, CMA-ES, DE).Strong knowledge of Bayesian Optimization and Gaussian Process modeling.Deep mathematical foundation (numerical methods, probability, linear algebra).Proficiency in the Python scientific ecosystem (NumPy, SciPy, pandas, scikit-learn).Demonstrated ability to design custom solvers for high-dimensional, ambiguous, or poorly behaved optimization landscapes.