Data Scientist
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.
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