Implement objective functions, constraint models, surrogate models, and penalty formulations.
Integrate optimisation techniques into ML workflows (hyperparameter tuning, black-box optimisation, surrogate modelling).
Conduct convergence, sensitivity, robustness, and stability analysis of optimisation methods.
Scale optimisation systems using Python, distributed computing, and numerical acceleration.
Communicate complex mathematical concepts to cross-functional audiences.
Required Qualifications
Master's / PhD in Operations Research, Applied Mathematics, Computer Science, Engineering, or related quantitative field.
Expertise in nonlinear, global, evolutionary, and multi-objective optimisation (e.g., NSGA-II / III, CMA-ES, DE).
Strong knowledge of Bayesian Optimisation and Gaussian Process modelling.
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 optimisation landscapes
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Data Scientist • New Brunswick, NJ, United States
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