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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