Conduct research and tackle open-ended data problems and questions Develop and implement new algorithms for data analysis and system optimization Assess next-generation technologies for machinery diagnostics and prognostics Apply machine learning algorithms and population-based meta-heuristic optimization methods Process and analyze large, multivariate datasets from equipment operations and manufacturing tests Develop data-driven algorithms for anomaly detection, failure prediction, and system optimization Collaborate with field and product engineers to identify key system health monitoring parameters Generate innovative ideas, establish new research directions, and execute technical projects Apply theoretical knowledge to solve real industrial problems Communicate ideas, plans, and results effectively via oral and written reports Contribute to technical discussions and maintain state-of-the-art knowledge in relevant fields Bachelor's or Master's degree in Computer Science, Statistics, Applied Mathematics, Physics, or a related quantitative field Basic knowledge of machine learning algorithms and optimization methods Familiarity with data analysis, data processing, and algorithm development Strong analytical, problem-solving, and computational skills Good communication and teamwork abilities Motivation to learn and grow in data science and industrial applications